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受賞論文では，商店街などの公共空間に設置した定点カメラ画像から歩行者間の相対運動を取得することにより，人流の特徴を定量化する手法を提案した．混雑と賑わいを区別することが可能か，という問いに対して，情報科学と産業数学から分野の垣根を超えた研究者が協働することで，主観アンケートや売上といった入手困難な情報を用いずに，賑わいを捉える指標を考するとともに，その妥当性をシミュレーションだけではなく，福岡市，川端通商店街のサポートを受けた実証実験を通して検証している．

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Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Publisher：Multimodal Technologies and Interaction  

Subtitles play a crucial role in facilitating the understanding of visual content when watching films and television programs. In this study, we propose a method for presenting subtitles in a way that considers cognitive load when viewing video content in a non-native language. Subtitles are generally displayed at the bottom of the screen, which causes frequent eye focus switching between subtitles and video, increasing the cognitive load. In our proposed method, we focused on the position, display time, and amount of information contained in the subtitles to reduce the cognitive load and to avoid disturbing the viewer’s concentration. We conducted two experiments to investigate the effects of our proposed subtitle method on gaze distribution, comprehension, and cognitive load during English-language video viewing. Twelve non-native English-speaking subjects participated in the first experiment. The results show that participants’ gazes were more focused around the center of the screen when using our proposed subtitles compared to regular subtitles. Comprehension levels recorded using LightSub were similar, but slightly inferior to those recorded using regular subtitles. However, it was confirmed that most of the participants were viewing the video with a higher cognitive load using the proposed subtitle method. In the second experiment, we investigated subtitles considering connected speech form in English with 18 non-native English speakers. The results revealed that the proposed method, considering connected speech form, demonstrated an improvement in cognitive load during video viewing but it remained higher than that of regular subtitles.

DOI： 10.3390/mti8060051

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Language：Others   Publishing type：Research paper (scientific journal)  

Federated learning (FL) has gained widespread attention as a distributed machine learning (ML) technique that offers data protection when training on local devices. Unlike conventional centralized training in traditional ML, FL incorporates privacy and security measures as it does not share raw data between the client and server, thereby safeguarding potentially sensitive information. However, there are still vulnerabilities in the FL field, and commonly used approaches, such as encryption and blockchain technologies, often result in significant computational and communication costs, making them impractical for devices with restricted resources. To tackle this challenge, we present a privacy-preserving FL system specifically designed for resource-constrained devices, leveraging compressive sensing and differential privacy (DP) techniques. We implemented the weight-pruning-based compressive sensing method with an adaptive compression ratio based on resource availability. In addition, we employ DP to introduce noise to the gradient before sending it to a central server for aggregation, thereby protecting the gradient's privacy. Evaluation results demonstrate that our proposed method achieves slightly better accuracy when compared to state-of-the-art methods like DP-federated averaging, DP-FedOpt, and adaptive Gaussian clipping-DP (AGC-DP) for the MNIST, Fashion-MNIST, and Human Activity Recognition data sets. Furthermore, our approach achieves this higher accuracy with a lower total communication cost and training time than the current state-of-the-art methods. Moreover, we comprehensively evaluate our method's resilience against poisoning attacks, revealing its better resistance than existing state-of-the-art approaches.

DOI： 10.1109/JIOT.2023.3347552

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Language：Others   Publishing type：Research paper (scientific journal)  

In modern transportation, autonomous driving technology has emerged as a significant innovation, offering improved safety, efficiency, and convenience. One of its core technologies is path planning, which creates an optimal route for vehicles in various traffic situations, ensuring they arrive safely and efficiently. However, existing path planning methods suffer from inflexible and inefficient road sampling, and limitations in path expression due to curve models. In this paper, we introduce a novel path planning framework that operates from a coarse-to-optimal approach. We first introduce an adaptive sampling method to construct a three-dimensional (3D) space-aware profiling map in the Frenet frame to quantify abstract traffic scenarios. This cost map guides the generation of waypoints by incorporating obstacle distributions in the s -direction and cost values in the l -direction. We then design a cost function to obtain the coarse path by considering ride comfort, anti-deviation from the center of the road, and path safety. Finally, a new path model is re-established based on the Taylor series, and a quadratic programming approach is employed to fine-tune the coarse path to attain the optimal path. Experimental results show that the proposed adaptive sampling method can dynamically adjust the sampling strategy according to the change in the number and distribution of obstacles on the road, significantly improving the sampling effectiveness rate to 100%, 99.85%, 99.68% and 99.61% for one-obstacle, two-obstacle, three-obstacle, and four-obstacle scenarios. Compared with the conventional method, the generated path length is shortened by 3.48% and 8.42%, and the maximum heading angle is reduced by 18.68% and 30.21% in the scenarios with obstacles of the same and different sizes.

DOI： 10.1109/ACCESS.2023.3347399

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Publisher：2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2024  

In recent years, with the trend towards shorter working hours, the quality of meetings has increasingly impacted these hours. Consequently, meeting quality and value become more important. To achieve better results in limited time, everyone needs to conduct meetings effectively. This requires skills such as facilitating the meeting, listening to others' opinions, accurately expressing one's own views, and using body language. However, these skills are currently only correlated with meeting effectiveness and lack comprehensive qualitative and quantitative assessments, leaving specific methods for improvement unclear. Additionally, the type of meeting can lead to participant stress or disinterest, making it essential to understand their psychological safety and engagement for more effective meetings. Measuring these psychological states poses significant challenges due to privacy and compliance concerns, particularly when using cameras or wearable sensors. This paper broadly addresses these issues and reports on our approach to detecting non-verbal cues, specifically nodding, from chair movements as a potential solution.

DOI： 10.1109/PerComWorkshops59983.2024.10502817

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Publisher：MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services  

Since office work has become large-scale and diversified in companies or organizations, work engagement and efficiency have been always an important index of a team's or group's evaluation because it is directly connected to their outcomes. In order to identify the group work context, we first need to recognize for what and how long the individual members are spending their time at their desks, but without privacy concerns and underestimation of their actual work. In this paper, we propose and evaluate the base system of personal desk activity recognition by using a low-cost compact WiFi node and its WiFi channel state information (CSI), which can lead to a lightweight group work context identification system. As a result, we achieved 94.2% desk activity recognition accuracy using the on-desk receiver, in recognizing five different classes.

DOI： 10.1145/3643832.3661452

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Publisher：MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services  

Indoor activity recognition using WiFi sensing is expected to have a wide range of applications, such as monitoring the elderly and home security. The state of radio wave propagation is called Channel State Information (CSI) and can be obtained using specific devices. By collecting CSI and applying machine learning, it is possible to recognize activities. However, CSI is sensitive to changes in the environment, so whenever the arrangement of furniture or the layout of the room changes, it is necessary to re-collect sample data and retrain the model. Retraining a model requires annotation work, which is costly in terms of time and effort. To address this issue, this paper proposes an annotation system that uses backscatter tags to reduce the cost of data collection and model training. In this system, a backscatter tag that generates a frequency shift depending on its angle is attached to a person during data collection, and activity recognition is performed by detecting the presence of the frequency shift. The backscatter tag-based recognition results are then used as pseudo-ground truth for model update.

DOI： 10.1145/3643832.3661451

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Publisher：IEEE Access  

Machine learning's exponential growth has transformed healthcare, with Federated Learning (FL) playing a pivotal role. Despite its significance, FL is vulnerable to privacy attacks. In response, researchers have integrated differential privacy (DP) into FL. Nevertheless, incorporating DP introduces challenges such as increased total communication costs and computational overheads due to the introduction of noise. This drawback renders FL with DP less viable for healthcare systems, characterized by numerous low-resource devices and network bandwidth constraints. To overcome this limitation, we propose integrating a Discrete Fourier Transform (DFT) aggregator post-noise addition to transform the gradient generated by local training before sending it to the central server. This process reduces the gradient size and provides rudimentary encryption. The evaluation results reveal the superior performance of our proposed method, demonstrating an enhanced accuracy ranging from 0.2% to 2% compared to existing differential privacy techniques, including RDP, DP-SGD, ZcDP, LDP-Fed, and DP-AdapClip. Our approach substantially reduces the total communication costs (ranging from 6% to 43% across different privacy budgets) with faster training times in healthcare datasets such as the PIMA Indian database and Breast Cancer Histopathology Images.

DOI： 10.1109/ACCESS.2024.3418016

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Publisher：International Conference on Information Networking  

As the high-speed wireless network spreads, the usage of shared Wi-Fi in places such as offices, coworking spaces, and homes has been increased. Along with the development and diversification of digital content, the amount of data consumed by individual devices has also grown. In situations where multiple users share limited network resources, some content that consumes a large amount of bandwidth like video streaming can potentially degrade the Quality of Experience (QoE) for other users nearby. Consequently, a need for persuasive intervention systems that encourage prosocial behavior is rising taking into consideration the QoE of other network users. To address this issue, this study proposes a "Persuasive Public Wi-Fi"that employs Wi-Fi to incrementally convince users towards more considerate usage of network resources. To be specific, we suppose that the persuasive public Wi-Fi must include three different modes: 1) a mode of normal networking, 2) a mode that can intervene with individual users through a captive portal, and 3) a mode that intentionally limits bandwidth, i.e., Quality of Service (QoS) control. This work demonstrates the design and implementation of the proposed system and presents the results of operational verification using a prototype system.

DOI： 10.1109/ICOIN59985.2024.10572181

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Publisher：MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services  

We present a secure decentralized learning framework suitable for resource-constrained devices within a cluster environment. Our approach focuses on enhancing privacy preservation during model aggregation by utilizing Differential Privacy. This technique adds random noise to gradients obtained from local training on edge devices before sending them for aggregation. This noise addition ensures that sensitive information within the gradients remains distorted, thus safeguarding user privacy. We showcase the implementation of our system on a cluster system employing Raspberry Pi 4 Model B devices, illustrating its feasibility and effectiveness in real-world scenarios. Through this demonstration, we highlight the practical applicability of our system in enabling secure decentralized learning within resource-constrained environments.

DOI： 10.1145/3643832.3661843

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Publisher：CEUR Workshop Proceedings  

In an era where digital technology profoundly influences our daily habits, we explore the potential of color psychology as a key element in the design of persuasive technology to promote health behavior change. This paper presents a strategy that uses color psychology to both encourage positive behaviors and discourage negative ones. We present two different applications: "eat2pic," a system designed to promote healthier eating habits by transforming meal selection into an engaging activity of adding color to images, and "color-wall," an application designed to reduce digital distractions by applying a grayscale filter to non-essential digital content. Through these demonstrations, we explore the efficacy and potential of color psychology in persuasive technology to influence user behavior in a health context and offer promising solutions to improve dietary choices and concentration while using computers.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE Access  

The conventional positioning methods, such as fingerprinting and trilateration, are commonly used in existing Wi-Fi positioning systems. Although the fingerprinting method offers relatively high accuracy, it faces challenges due to its sensitivity to environmental changes and the necessity of extensive training data and calibration. The trilateration method calculates positions based on the distances between anchors and targets. However, inaccuracies in measuring these distances could significantly impact the overall accuracy. Additionally, the necessity for at least three anchors creates a requirement for a more extensive infrastructure, posing challenges to practical service deployment. In this paper, we introduce <italic>bilatangulation</italic>, a novel cluster-based double-step positioning method that leverages distances calculated using fine timing measurement (FTM) and angles determined using channel state information (CSI) from two anchors. The first step addresses the symmetry problem of the two intersections in distance-based positioning by utilizing the angle orientations. In the second step, we performed measurement error compensation by clustering multiple intersections generated from both distance and angle data, taking into account the characteristics of each cluster. Our practical experiment was conducted indoors using off-the-shelf network interface card (NIC). For positioning, only two anchors were employed, resulting in an original mean positioning error (MPE) of 1.58 <italic>m</italic>. Applying a measurement error compensation step reduced the final MPE by 88% compared to the original MPE.

DOI： 10.1109/ACCESS.2024.3447112

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Language：English   Publisher：International Institute of Applied Informatics  

DOI： 10.52731/liir.v005.302

CiNii Research

Publisher：2024 8th International Conference on Robotics, Control and Automation, ICRCA 2024  

Automated driving technology has garnered increasing attention due to its potential to reduce traffic accidents, alleviate congestion, and enhance travel convenience. As a key component of automated driving technology, trajectory planning plays a pivotal role in determining feasible trajectories in different traffic scenarios. However, there is currently insufficient research on the performance of different curve models in lane-changing scenarios. Furthermore, the conventional speed planning method (DP&QP) often ignores vehicle kinematics and traffic scenarios when generating convex spaces, resulting in inefficiency and longer run time. In path planning, this paper compared the performance of Dubins, Sine, Bézier, and B-spline curves in lane change scenarios, ultimately selecting the B-spline curve as the lane change path model to ensure that the vehicle remains close to the center line of the road after a lane change. In speed planning, we introduce a novel method for directly generating a convex space, taking into account the kinematics of the ego vehicle and the current traffic conditions. This approach allows for adaptive adjustments to the speed planning scheme based on the traffic situation. Compared with the conventional trajectory planning method, this approach significantly reduces the run time by 32.7%.

DOI： 10.1109/ICRCA60878.2024.10649189

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Language：English   Publisher：International Institute of Applied Informatics  

DOI： 10.52731/liir.v005.312

CiNii Research

Publisher：2024 8th International Conference on Robotics, Control and Automation, ICRCA 2024  

Dense video captioning tasks are used to detect interesting events and provide descriptive text for these events from untrimmed videos. This technology has the potential to be used in security surveillance and human care applications. However, current methods often overlook the relationships between objects in the video, which limits their applicability and makes it challenging to adapt them to specific domains, such as video summarization for indoor human activities. In these scenarios, human activities are closely intertwined with the objects in the scene. In this paper, we propose a plug-and-play module designed to enhance existing dense video captioning methods with spatial information. Specifically, we extract spatial information about the interesting objects using Red-Green-Blue-Depth (RGB-D) images and the results of image segmentation. We then integrate this information into the captions generated by the Dense Video Captioning (DVC) method using a fine-tuned Large Language Model (LLM). We evaluate the performance of our model on a custom dataset and demonstrate that our system provides a convenient and effective approach for obtaining space-enhanced captions.

DOI： 10.1109/ICRCA60878.2024.10649311

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Language：Others   Publishing type：Research paper (other academic)  

Understanding user emotion is essential for Human-AI Interaction (HAI). Thus far, many approaches have been studied to recognize emotion from signals of various physiological modalities such as cardiac activity and skin conductance. However, little attention has been paid to the fact that physiological signals are influenced by and reflect various factors that have little or no association with emotion. While emotion is a cross-modal factor that triggers responses across multiple physiological modalities, features used in existing approaches also reflect modality-specific factors that affect only a single modality and have little association with emotion. To address this, we propose an approach to extract features that exclusively reflect cross-modal factors from multimodal physiological signals. Our approach introduces a multilayer RNN with two types of layers: multiple Modality-Specific Layers (MSLs) for modeling physiological activity in individual modalities and a single Cross-Modal Layer (CML) for modeling the process by which emotion affects physiological activity. By having all MSLs update their hidden states using the CML hidden states, our RNN causes the CML to learn cross-modal factors. Using real physiological signals, we confirmed that the features extracted by our RNN reflected emotions to a significantly greater extent than the features of existing approaches.

DOI： 10.1007/978-981-99-7019-3_40

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Other Link： https://dblp.uni-trier.de/db/conf/pricai/pricai2023-1.html#IshikawaKNNIMA22

Language：Others   Publishing type：Research paper (other academic)  

In this demo, we will show our proposed system that can count nodding without either a camera or any sensor attached to the person. Our proposed system capitalizes on the fact that the upper body moves in conjunction with nodding and that this body motion slightly shakes the chair. We explore the challenge of recognizing nodding from the extremely subtle sway of a chair. To recognize nods in real-Time, we employed a supervised learning approach using acceleration data from sensors attached to the chair's backrest. Ultimately, the Support Vector Machine (SVM) achieved a nodding recognition accuracy of 0.990. Further testing of the accuracy of nodding frequency measurements yielded an accuracy of 0.947, suggesting that the optimal position for the accelerometer is the backrest. These results indicate that simply placing the accelerometer on the backrest can effectively quantify the frequency of nods from seated participants.

DOI： 10.1145/3627050.3630740

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Language：Others   Publishing type：Research paper (other academic)  

Aiming to evaluate the concentration and engagement level in offline group discussion scenarios, we seek to capture the interaction information among meeting participants. In this paper, we focus on the facial orientation and present an end-To-end hybrid system capable of 2D overhead facial orientation estimation. Compared to common perspectives from which personal identity information is exposed, we explore approaches to perform overhead facial orientation estimation due to the privacy problem. The proposed system is a neural network-based architecture and composed of three modules. First we leverage and train from scratch the YOLOv8 model on the HollywoodHeads dataset, which allows full-perspective head detection in visual scenes. With the obtained head location, we then generate the corresponding head masks from the visual information within the bounding box. Finally, we fit them to ellipse so that 2D overhead facial orientation of all the meeting participants in the visual scenes can be estimated with the ellipse properties. Experiments demonstrate that our system achieves an MAE of 12.47 on the test dataset.

DOI： 10.23919/ICMU58504.2023.10412258

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Language：Others   Publishing type：Research paper (other academic)  

In this paper, we propose a method to measure human head motion, especially nodding, without attaching any sensors to the person. Our proposed system focuses on the fact that the upper body moves along with nodding and that the body motion slightly shakes the chair. We challenge the problem of whether it is possible to recognize a nodding from the extremely slight sway of a chair. To reveal the optimal position of sensors, we collected data by attaching multiple accelerometers to various positions on a chair, including the backrest, the seat's underside, and the legs. Using a supervised learning approach, we determined the best positions and combinations of sensors for recognizing nodding more collectively. The Support Vector Machine (SVM) achieved a nodding recognition accuracy of 0.990. Further testing of the accuracy of nodding frequency measurements resulted in an accuracy of 0.947, suggesting that the best position for the accelerometer is the backrest. These results suggest that simply placing the accelerometer on the backrest can effectively quantify the nod frequency of seated participants.

DOI： 10.23919/ICMU58504.2023.10412249

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Language：Others   Publishing type：Research paper (scientific journal)  

Quadriplegics face a communication obstacle as their physical abilities are restricted, leaving them unable to speak or use their limbs, with only their upper neck being mobile. So, we propose a recognition system and a new communication language utilizing Morse code and head movements, to break this barrier. We aim to overcome the limitations of camera-based and wearable-sensor methods, including occlusion, privacy concerns, and user inconvenience. The goal is to passively detect quadriplegics' head movements and map them to their corresponding character. The dataset including all 26 alphabet letters, was gathered in various settings, including single-user and multi-human environments, with multiple locations for each setting. For evaluation, 2% samples are randomly selected from the unseen environment to be used with the seen environment as a training dataset. Based on the results, our system demonstrates practical feasibility for real-world implementation, with accuracy rates of 94% and 80% achieved in single-user and multi-human environments, respectively.

DOI： 10.1109/ACCESS.2023.3326259

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In recent years, many studies explore CSI-based Wi-Fi sensing produce high accurate results in device-free localization, human activity recognition. Channel state information (CSI) represents how wireless signals propagate from transmitter to receiver and provides rich information to identify human presence. By collecting CSI from the specific devices, the machine learning model can be trained to recognize the human activity. However, in multi-room residential settings where walls and furniture obstruct signals, effective coverage with a limited number of transceivers becomes a crucial challenge, underlining the importance of their optimal placement. In this paper, we deployed multiple transceivers in a smart home environment in our university and studied transceiver arrangement patterns for CSI-based indoor localization and activity recognition. We compared the accuracy of localization and activity recognition for a total of 18 patterns consisting of up to five transceivers. In the activity recognition, we used Support Vector Machine (SVM) to classify whether or not a person is moving. The results show that the pattern using only one pair of transceivers achieved an accuracy 85% and covered the entire house. Meanwhile, in the localization we used Light Gradient Boosting Machine (LGBM) to classify which room the person is. The results show that accu-racy decreases as the number of devices is reduced. Therefore, we investigated deployment pattern to achieve accuracy with smaller number of transceivers.

DOI： 10.23919/ICMU58504.2023.10412230

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Language：Others   Publishing type：Research paper (other academic)  

In this paper, we propose ToonMeet, a hybrid frame-work for high-resolution and style-controllable online meeting toonification that ensures real-time operation speed. ToonMeet applies video frame interpolation to traditional portrait toonification pipelines, allowing for the synthesis of intermediate frames between adjacent toonified keyframes, significantly accelerating the overall process and saving computational resources. However, this approach brings a new problem, where prevailing flow-based video frame interpolation methods tend to cause more ghost and blur artifacts in toonified scenes compared to non-toonified scenes, especially when fast-moving objects exist. We study this previously undiscussed problem and explore its causes. To address this, we introduce a new dataset called TM3B (Toonified Multi-modal Meeting Behaviors), offering high-resolution and cross-platform multi-modal stylized meeting data of Japanese youth in various scenarios. Then, we fine-tune ToonMeet on these tailored data and the resulting model presents improved optical flow estimation ability on toonified videos. Extensive experiments demonstrate that ToonMeet can achieve great spatiotemporal performance and perform high-quality toonification of online meetings with real-time operation speed.

DOI： 10.1109/ICTAI59109.2023.00013

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Nowadays, there is a growing demand to understand the mental well-being of office workers, driven by increased awareness of its impact on productivity and the need for healthier work environments. Recently, the use of Wi-Fi channel state information (CSI) for activity recognition has received significant attention due to its wide availability and privacy protection. In this paper, we propose a passive desk body gesture recognition system that utilizes Wi-Fi CSI from an ESP32 toolkit to automatically detect the worker's mood and emotions. The system is designed to operate within the Internet of Things ecosystem, employing a low-energy device to collect and compress CSI measurements, resulting in improved energy efficiency and cost-effectiveness. The proposed system demonstrates high recognition accuracy of over 98 % in-session and 72 % out-session evaluations.

DOI： 10.23919/ICMU58504.2023.10412222

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Language：Others   Publishing type：Research paper (other academic)  

Nowadays more and more people are wearing smart-watches in their daily lives. The various sensors embedded in smartwatches bring the ability to evaluate users' status as well as the risk of privacy issues. For example, if users are typing on key-boards while wearing smartwatches, the attacker could know the typing contents from the sensor data collected by the malicious applications that are installed on the targets' smartwatches. In this paper, we propose WatchLogger, the framework using audio and accelerometer signals to recognize the English words being typed, for demonstrating how to implement the smartwatch-based side-channel attack. Different from the previous studies that focused on the recognition of each key or pair of keys being pressed, WatchLogger aims to perform recognition on the scale of words. To achieve this goal, WatchLogger exploits the audio signals for segmentation and the accelerometer signals for classification. In addition, we propose an ensemble classification model to deal with the problem caused by too many words. At last, we build the dataset WTW-100 with 100 classes of words and 100 samples for each class, and we conduct experiments on the dataset. The experimental results show an accuracy of 98.5 % for keystroke recognition and 91.5 % for word classification, showing a considerable performance of WatchLogger.

DOI： 10.23919/ICMU58504.2023.10412218

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Internet use is expanding along with the spread of smartphones and the promotion of digital use, but information security literacy is required to use the Internet safely. This paper addresses these challenges by focusing on the potential of microlearning and nudge theory to support continuous learning to solve these problems and presents a novel learning system that utilizes a Wi-Fi landing portal that appears as a dialog or a browser. To assess the effectiveness of the proposed system, we conducted a comparative analysis of information security test scores before and after the operation of the learning system and questionnaire-based evaluation. The results showed that the analysis of answer scores before and after the operation of the learning system was significant (\mathrm{p} < 0.05), indicating an improvement in scores. It was also found that using interface information with nudges effectively motivated participants to engage in repeated learning behaviors. However, it should be noted that frequent interventions might reduce acceptability, and the psychological burden arising from task interruptions needs to be taken into account.

DOI： 10.23919/ICMU58504.2023.10412210

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When a tourist wants to visit a site, he/she first asks about the category of the site. The availability of detailed information about the site allows him/her to tailor his/her visit to his/her preferences. This information is therefore essential for Point of Interest (POI) recommendation. However, it is rarely or never available for lesser-known POIs. Lesser-known POIs can be considered as places or events that local people know well, and that may be important to them, but that others do not know about. We propose an approach to estimate the categories of lesser-known POIs based on information from social media. The originality of this approach lies in the extraction of information and links between them, the encoding of the POIs, the representation of the data, and the combination of machine learning techniques such as Few Shot Learning, LightGCN, and Clustering for the estimation of POI categories. The results of the experiments would allow us to confirm that our approach can estimate POI categories and thus discover information about POIs that may be relevant. This approach would be useful for our future work on POI recommendations.

DOI： 10.1109/WI-IAT59888.2023.00020

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Autonomous vehicles are an essential tool for promoting the development of intelligent transportation systems (ITS) and can effectively reduce traffic accidents caused by human errors. As an important part of the automatic driving software system, path planning is responsible for generating the motion trajectory of the vehicle, which is the primary factor determining driving quality. However, solution space construction and optimization problem formulation remain challenging research areas in the field of path planning. In this paper, we propose a multi-objective optimization algorithm for static obstacle avoidance to improve the comfort, safety and anti-deviation of the planned trajectory. We decouple the lateral and longitudinal motion of the vehicle using the Frenet frame and discretize the driving state space to generate target states of the vehicle. Based on the initial and target states, we generate a set of lateral and longitudinal motion trajectories using quintic and quartic polynomials, respectively. In addition, we design a cost function that comprehensively considers the comfort, safety, and deviation distance of the road center line by combining an acceleration check, curvature check, and collision check. As part of the cost function, we propose a novel method to quantify the safety of candidate trajectories considering the size of obstacles. The experimental results show that the proposed algorithm can quantize the safety of candidate paths and improve comfort 13.47%, 32.19%, 59.36% and 18.60% on a straight road, curvy road, intersection and U-shaped road, respectively. Furthermore, the algorithm can improve anti-deviation by 63.72%, 13.86%, 44.36%, and 45.56% on a straight road, curvy road, intersection and U-shaped road, respectively.

DOI： 10.1109/ACCESS.2023.3294713

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Federated learning provides techniques for training algorithms using mobile or decentralized devices, in contrast to traditional machine learning in which algorithm training is performed on centralized devices. In addition, federated learning provides privacy and security features, as the client and server do not share raw data, which may contain confidential information. A number of studies have shown, however, that using federated learning alone is not enough to protect data privacy in certain situations. To overcome this problem, differential privacy is proposed, which is a technique in which artificial noise is added to the raw data. By implementing this method, a high level of privacy protection can be obtained, however this added noise also reduces model accuracy. To address this issue, this paper proposes a new approach to implement differential privacy in federated learning using adaptive Gaussian clipping. We implemented the method by tightening the privacy budget, and introducing dynamic sampling probability, adaptive clipping based on hyperparameters, and a new privacy loss calculation. Our method's main objective is to adaptively change the amount of noise given to the model, thereby maximizing the model's accuracy performance, while maintaining privacy protection levels. Evaluation results show that our proposed method presents slightly better accuracy when compared to other existing differential privacy variants such as RDP, DP-SGD, and ZcDP, for both balanced (i.i.d.) and unbalanced datasets (non-i.i.d.), for a lower total communication cost than some variants.

DOI： 10.1109/MDM58254.2023.00042

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Other Link： https://dblp.uni-trier.de/db/conf/mdm/mdm2023.html#HidayatNDA23

Language：Others   Publishing type：Research paper (other academic)  

Federated learning has gained popularity as a distributed machine learning approach that provides security and privacy for data trained on local devices. However, vulnerabilities still exist in this approach, and common solutions such as encryption and blockchain techniques often suffer from high computation and communication costs, making them impractical for resource-constrained devices. To solve this problem, we propose a privacy-preserving federated learning system that leverages compressive sensing and differential privacy, specifically designed for devices with limited computational resources. In this paper, we demonstrate the capabilities of our proposed system in resource-limited environments. We outline the features, infrastructure, and algorithm of our proposed system, and simulate its performance using image datasets on a Raspberry Pi 4 and an Android smartphone in a cloud environment. Our approach offers a practical solution for secure and privacy-preserving federated learning in resource-constrained scenarios, with potential applications in various domains such as healthcare, IoT, and edge computing.

DOI： 10.1109/MDM58254.2023.00038

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Other Link： https://dblp.uni-trier.de/db/conf/mdm/mdm2023.html#HidayatNA23

Language：Others   Publishing type：Research paper (other academic)  

Human activity recognition (HAR) using the deep learning method has caught the attention of researchers thanks to its automatic feature extraction and accurate prediction capabilities. However, for applications based on a wearable sensor, such as an inertial measurement unit (IMU), the process of collecting and hand-labeling large amounts of data is complicated and labor-intensive, meaning that there is a limited amount of data available for model training. Therefore, there is a need to propose and develop data augmentation approaches to generate high quality data for the growth of HAR research. We propose a head-mounted virtual gyroscope signal generator to alleviate the problems caused by the lack of data in head movement-related applications. Unlike previous work, our system only generates head-motion related gyroscope data, minimizing system complexity. We trained a deep-learning model in a head motion-based application with different generated sensor data ratios, and show the viability of our proposed data generation method.

DOI： 10.1109/ICCE-Taiwan58799.2023.10227010

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Other Link： https://dblp.uni-trier.de/db/conf/icce-tw/icce-tw2023.html#LuCDNA23

Language：Others   Publishing type：Research paper (other academic)  

In this paper, we propose a purchasing support system for offline stores that can present information to users without being evasive by interactively presenting a small amount of product information according to their interest in the product as predicted by the system. We design and implement a proof-of-concept system for an actual use case scenario: a snack purchasing area in an office. We evaluated the effectiveness of the proposed system through a snack purchasing experiment with 11 participants. Experimental results suggest that the proposed method can induce user interest and encourage viewing and that some users may receive and consider the information presented while making a purchase.

DOI： 10.1007/978-3-031-30933-5_8

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Language：Others   Publishing type：Research paper (other academic)  

Publishing type：Research paper (international conference proceedings)  

In this study, the target behavior of prosocial behavior was defined as garbage disposal (behavior of taking out the trash), and an intervention method using an inducement was proposed and evaluated. Specifically, we conducted an intervention in which the saturation of a trash can was visualized using a colored light that could be changed to red, yellow, and green. In addition, questionnaire surveys were administered before and after the intervention to 28 undergraduate and graduate students and faculty members, asking about their attitudes and feelings toward garbage disposal behavior, and personality traits. The results of a survey on the frequency of trash disposal be-fore and after the experiment showed that the time that the saturated trash can was left unattended was reduced by 81%. The questionnaire survey confirmed that the intervention increased the positive attitude toward trash disposal behavior, indicating that the intervention had an impact on trash disposal behavior. In addition, we were able to confirm the characteristics of individual personality traits and susceptibility to intervention.

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Language：Japanese  

近年，社会課題解決のための行動変容を促す様々な介入手段が検討されており，その中でも他人や他のの団体，社会のために行う行動である，向社会的行動の促進技術の研究が注目を浴びている．向社会的行動促進に関する従来研究では，短期的ではあるが大きな効果が期待できる，『誘因』による向社会的行動の促進要因が何であるか考察が十分になされていない．そこで本研究では，向社会的行動の対象行動をゴミ捨てと定め，誘因による介入手法の提案，及び被験者の行動とアンケート調査による評価を行った．具体的には，大学生・大学院生・教員28名を対象として，ゴミ箱の飽和度を赤，黄色，緑の色に変えられるカラーライトで可視化する誘因介入を行い，介入前後に個人のパーソナリティ特性や心理，態度を評価するアンケート調査を実施した．実験結果より誘因が対象行動に与える効果，及び介入による影響の受けやすさと各パーソナリティ特性や心理，態度との関係性を分析する．ゴミ捨て頻度の調査の結果，飽和したゴミ箱が放置される時間が83％短縮される結果となった．アンケート調査からは，介入によるゴミ捨て行動への積極性の向上が確認され，介入がゴミ捨て行動に影響を与えることが明らかとなった．一方で，個人の性格特性と介入による影響の受け方に大きな相関関係は見られず，本研究での性格特性の分析手法では，課題が発生する可能性があると考えらた．
In recent years, various intervention methods to promote behavioral change for solving social problems have been studied, and among them, research on techniques to promote prosocial behavior, which is behavior performed for the benefit of others, other groups, and society, has attracted much attention. Conventional studies on the promotion of prosocial behavior have not sufficiently examined the factors that promote prosocial behavior by "inducements," which are expected to have a short-term but significant effect. In this study, the target behavior of prosocial behavior was defined as garbage disposal, and an intervention method with incentives was proposed and evaluated by the subjects' behavior and questionnaire surveys. Specifically, we conducted an incentive intervention in which the saturation of trash cans was visualized using colored lights that could be changed to red, yellow, and green, and a questionnaire survey was conducted before and after the intervention to evaluate individual personality characteristics, psychology, and attitudes. The results of the experiment show the effect of the inducement on the target behavior, and the relationship between the susceptibility to the intervention and each personality trait, psychology, and attitude. The results of a survey on the frequency of garbage disposal showed that the time that saturated garbage cans were left unattended was reduced by 83&#37;. The questionnaire survey confirmed that the intervention increased the positive attitude toward trash disposal behavior, indicating that the intervention had an impact on trash disposal behavior. On the other hand, there was no significant correlation between the personality characteristics of the individuals and the way they were affected by the intervention, suggesting that the analysis method of personality characteristics in this study may have some problems.

Language：Others   Publishing type：Research paper (other academic)  

Wi-Nod: Head Nodding Recognition by Wi-Fi CSI Toward Communicative Support for Quadriplegics
Recently, the studies of wireless device-free human sensing technology have dramatically advanced with enabling a variety of applications, from activity recognition to vital sign monitoring. In this paper, we propose Wi-Nod which leverages the Wi-Fi Channel State Information (CSI) to detect head nodding gestures for each Morse code symbol based on time-frequency features for accurate recognition accuracy in multi-human context environment. The system consists of three basic modules: data collection, data preprocessing, and learning part based on the inception model. The model was trained to perform the head movement detection based on the CSI spectrogram collected by the ESP32 nodes. We evaluated the performance of the system on four different data sets collected in two different sessions. Our system achieves over 95&#37; recognition accuracy that reveals the feasibility of Wi-Nod system for real-life deployment.

DOI： 10.1109/WCNC55385.2023.10118666

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Language：English   Publishing type：Research paper (other academic)  

Differential Privacy with Weighted ϵ for Privacy-Preservation in Human Activity Recognition
Many services based on human activity recognition (HAR) have been developed; however, user activity data include a large amount of private information. Although privacy protection is important in activity recognition, it has not been sufficiently explored. Therefore, we propose a privacy-preserving mechanism for HAR services that uses differential privacy. The proposed method reduces the user recognition accuracy to a level that satisfies the privacy requirements by adding weighted noise to the features in the learning model construction and then improves the activity recognition accuracy (service usefulness). The results indicate that when the privacy requirement is defined as less than the probability of a user being identified by chance, the proposed method improves the activity recognition accuracy by approximately 10 &#37; compared to the conventional method.

DOI： 10.1109/PerComWorkshops56833.2023.10150239

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Language：English   Publishing type：Research paper (other academic)  

C-HAR: Compressive Measurement-Based Human Activity Recognition

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

<p>In recent years, with the spread of wearable devices and their built-in sensors, the analysis of time-series heartbeat data has been promoted in research in the healthcare field. Because time-series heartbeat data can be used to detect abnormal heartbeats and estimate psychological stress, there are many medical and healthcare services that use heartbeat data. However, time-series heartbeat data contains a lot of privacy information, which requires careful consideration of privacy protection for users of these services. In this study, we proposed and evaluated a privacy-preservation mechanism using ``differential privacy'' for a service that detects abnormal heartbeats based on time-series heartbeat data. The evaluation results show that the proposed method is more effective than conventional differential privacy-based mechanisms in balancing service usefulness while realizing user privacy protection.</p>

DOI： 10.11517/pjsai.jsai2023.0_1l4os18a03

CiNii Research

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Web of Science

Publisher：ISKE 2023 - 18th International Conference on Intelligent Systems and Knowledge Engineering  

Local events are important for the vitality and dynamism of a city. However, some events or sites are lesser-known or only known by the local community. The information about them is then limited or not available. To address this issue, we propose an approach to automatically generate knowledge graphs from information posted on social networks and data on human mobility. This graph is generated from the tweets using the syntactic relationships between the verbs, nouns, and adjectives. Its exploitation would allow to estimate the categories of lesser-known Points of Interest (POIs) or events. It can also allow to generate sentences and to answer questions that tourists may ask. The specificity of our approach lies in the Graph Convolutional Network (GCN)-based encoding of POIs with a graph of verbs, nouns, and adjectives and the estimation of POI categories using the proposed encoding. Experimental results show that our approach performs better than approaches in the literature.

DOI： 10.1109/ISKE60036.2023.10481229

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Web of Science

Publisher：CEUR Workshop Proceedings  

In this study, the target behavior of prosocial behavior was defined as garbage disposal (behavior of taking out the trash), and an intervention method using an inducement was proposed and evaluated. Specifically, we conducted an intervention in which the saturation of a trash can was visualized using a colored light that could be changed to red, yellow, and green. In addition, questionnaire surveys were administered before and after the intervention to 28 undergraduate and graduate students and faculty members, asking about their attitudes and feelings toward garbage disposal behavior, and personality traits. The results of a survey on the frequency of trash disposal be-fore and after the experiment showed that the time that the saturated trash can was left unattended was reduced by 81%. The questionnaire survey confirmed that the intervention increased the positive attitude toward trash disposal behavior, indicating that the intervention had an impact on trash disposal behavior. In addition, we were able to confirm the characteristics of individual personality traits and susceptibility to intervention.

Scopus

Publisher：2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023  

In this paper we present C-HAR, a low-cost,low-complexity, compressive measurement-based human activity recognitionframework for embedded devices, capable of simultaneously filtering andacquiring the sensor readings of a smartwatch's accelerometer and gyroscope atsub-Nyquist rates. A software simulation of the full system obtains an accuracyof 92.0% and 88.0% for the accelerometer-based and gyroscope-based systemsrespectively, obtained at a 5 Hz sample rate. A microcontroller implementationof the system's back-end obtains an accuracy of 90.3% and 87.1% for theaccelerometer-based and gyroscope-based systems respectively with a runtimeover twice as fast as that of a comparable baseline system.

DOI： 10.1109/PerComWorkshops56833.2023.10150315

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Language：Others   Publishing type：Research paper (scientific journal)  

Learning user attributes is essential for providing users with a service. In particular, for e-commerce portals which deal in variety of goods ranging from clothes to foods to home electronics, it is especially important to learn 'domain-independent' attributes such as age, gender, and personality that affect people's behavior across various domains of daily life (e.g., clothing, eating and housing) because these attributes can be used for personalization in diverse domains their service covers. Thus far, researchers have proposed approaches to learn user representation (UR) from user-item interactions, trying to embed rich information about user attributes in UR. However, very few can learn URs that are domain-independent without confounding them with domain-specific attributes (e.g., food preferences). This could consequently undermine the former's utility for personalizing services in other domains from which the URs are not learned. To address this, we propose an approach to learn URs that exclusively reflect domain-independent attributes. Our approach introduces a novel multi-layer RNN with two types of layers: Domain Specific Layers (DSLs) for modeling behavior in individual domains and a Domain Independent Layer (DIL) for modeling attributes that affect behavior across multiple domains. By exchanging hidden states between these layers, the RNNs implement the process of domain-independent attributes affecting domain-specific behavior and makes the DIL learn URs that capture domain-independence. Our evaluation results confirmed that the URs learned by our approach have greater utility in predicting behavior in the other domains from which these URs were not learned thereby demonstrating adaptability to various domains.

DOI： 10.1109/ACCESS.2022.3220781

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Language：Others   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/BigData55660.2022.10020753

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Other Link： https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2022.html#OchiaiSKIOYYOSA22

Language：Japanese  

カメラやマイクをミュートにしたオンライン会議で参加者の反応を知ることは困難である．対話相手からのフィードバックが少ない状況では，話者は話しづらさを感じ，聴者は会議への積極性が低下するといった課題がある．我々は，オンライン会議で不足している対話相手からのフィードバックを補填するために，参加者の表情認識から推定された感情を絵文字としてリアルタイムにフィードバックするシステム「REmotion」の開発を行っている．我々の目的は，本システムをオンライン会議で用いることで，話者の話しづらさを解消することと聴者の会議への積極性を向上させることである．そのために，参加者が対話相手を意識して表情を変化させることが重要だと考える．本論文では，開発したシステムを用いて実験を行い，参加者がカメラやマイクをミュートにして参加するオンライン会議で本システムを用いると，参加者が自分の表情を意識的に変化させることを明らかにした．

Language：English   Publishing type：Research paper (other academic)  

Function Estimation of Multiple IoT Devices by Communication Traffic Analysis

Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2022.3194874

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Language：Japanese  

路線バスは沿線の道路交通状況や天候など，外的要因により運行状況が左右されやすいため，遅延することが多い傾向にある．このため，他の交通機関への乗り換えが予定通りにできなくなる事例が観察されている．本研究では，路線バス走行履歴データから運行状況を解析し，乗り換え時間の設定を変えた場合にどのように乗り換えが成立するかを調査した．また，利用客の満足度を高めるような乗り換えを実現するために，改善が必要なダイヤを検出する指標の改良を検討した．

Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (other academic)  

Color-wall: Adaptive Color Filter to Reduce Digital Distractions during PC Work
As the Attention Economy has grown, numerous digital contents are made with the intention of capturing users' attention and spending more of their time by the use of attractive graphics and color psychology. In this paper, we propose a novel defense strategy, called a color-wall, to block digital distraction brought on by the consumption of visually seductive and addictive digital content. When it detects that a user is viewing media or using applications unrelated to the main task, it removes colors from the screen (gray-scaling) to make them less aesthetically pleasing and directs the user away from looking at them. We implemented a mac OS software as a proof-of-concept for the color-wall and conducted user testing (N=14) to assess the effectiveness of the proposed strategy. The evaluation results suggest that the color-wall is an effective strategy for reducing unwanted digital distractions such as browsing restricted content (YouTube, Twitter, etc.) while working on a PC.

DOI： 10.1145/3544793.3563403

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Language：Others   Publishing type：Research paper (scientific journal)  

Many office workers are exposed to fluorescent light from mid-morning to after dark. The changes in the amount of light in the natural world, from bright during the day to dark at night, play an important role in maintaining a regular daily rhythm. In this study, we evaluated the effects of LED lighting equipped with a circadian rhythm control function, Lavigo, by comparing it with conventional fluorescent lighting on students' sleep quality and stress. Lavigo's Visual Timing Light (VTL) function adjusts the color and brightness of light according to the biological rhythm of about 24 h cycles. We measured the sleep state using Fitbit and collected subjective responses through a stress-checking questionnaire. A salivary amylase monitor was used to measure objective stress values from a medical perspective. No significant difference in stress values was observed during the experiment. However, only during the week that Lavigo was used, a certain subject showed a significant decrease in waking time during sleep, and another subject showed an increase in deep sleep. It was also found that these subjects tended to lead an irregular life, such as staying up until midnight.

DOI： 10.18494/SAM3952

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Maintaining a positive interaction is the key to a healthy and efficient meeting. Aiming to improve the quality of online meetings, we present an end-to-end neural-network-based system, named MeetingPipe, which is capable of quantitative microbehavior detection (smiling, nodding, and speaking) from recorded meeting videos. For smile detection, we build a neural network framework that consists of an 18-layer residual network for feature representation, and a selfattention layer to explore the correlation between each receptive field. To perform nodding detection, we obtain head rotation data as the key nodding feature. Then we use a gated recurrent unit followed by a squeeze-and-excitation mechanism to capture the temporal information of nodding patterns from head pitch angles. In addition, we utilize TalkNet, an active speaker detection model, which can effectively recognize active speakers from videos. Experiments demonstrate that with K-fold cross validation, the F1 scores of the smile, nodding, and speaking detection are 97.34, 81.26, and 94.90%, respectively. The processing can be accelerated with multiple GPUs due to the multithread design. The code is available at https://github.com/ humanophilic/MeetingPipe.

DOI： 10.18494/SAM3959

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Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00034

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Other Link： https://dblp.uni-trier.de/db/conf/ithings/ithings2022.html#WatanabeMNAI22

Language：Japanese  

本研究では，行動変容を誘発する要素技術として，ゲーム等で利用される ''抽選'' がもたらす高揚感に着目し，ゲーム内で使用される抽選器を構成するどの要素が人の高揚感に影響を与えるかについて調査した．我々は，スロットを止めるボタンを押すという行為や手回しで抽選器を操作する行為が自己決定感を生み出し，さらに制御幻想を生じさせているのではないかと仮説を立て，抽選器として，手回し式の抽選器，自動でリールが停止するスロット，手動でリールを停止するスロットの 3 つの種類の抽選器を作成した．そして，実際のゲーム上で不特定多数の挙動を見るオンライン実験（アンケート回答者 428 名）と，表情筋や心拍など生理現象を定量的に計測するオフライン実験（被験者 1 名）を行った．その結果，スロットを自分で止めるという操作が制御幻想を生み出し，自己決定感が高揚感の向上に関係している可能性が示唆された．また，リーチによるニアミスなど当たりに近いハズレの存在も，ユーザの高揚感や表情の変化に影響を与える重要な要因であることが明らかとなった．

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/IIAIAAI55812.2022.00034

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Other Link： https://dblp.uni-trier.de/db/conf/iiaiaai/iiaiaai2022.html#RajaonarivoMA22

Language：English  

A study on estimating the accurate head IMU motion from Video

Language：English  

A Study on 3D Gaze Recognition from the Environment Side by Multiple Optical Sensors

Language：Japanese  

近年，一般家庭にも IoT デバイスが普及し，リモコンや照明，ドアロック，コンセントなど様々な機能を持つ IoT デバイスが販売され，様々な場面で活用されている．現在の IoT デバイスは動作がブラックボックスであり，IoT デバイスがユーザの意図しない通信を行っていた場合に気づく術がない．そこで，我々は IoT デバイスがどのような通信を行っているかを検知し，それをもとに適切な通信のみ許可することができるアクセス制御の機能と IoT デバイスがどのような通信を行っているか可視化することでユーザが IoT デバイスの動作状況を理解することを可能にする機能を持つシステム (IoT 活動量計) の実現を目指している．その実現のため，本研究では，IoT デバイスの通信トラヒックを分析し，どの機能が使われているかを推定する手法を提案し，4 種別の IoT デバイス各 2 機種ずつの計 8 機種の 8 種類の機能の通信トラヒックを収集し，分類を行った．その結果，デバイスの機種と実行した機能の組み合わせの 16 通りで分類した場合，88% の精度で機能を推定できることを確認した．また，実行した機能のみの 8 通りで分類した場合，69% の精度で機能を推定できることを確認した．

Language：Japanese  

オンライン購買システムは，ページ遷移やクリックの履歴から購買者が商品の購買に至るまでの過程を記録することができる．これにより，興味がある商品や比較検討した商品がわかり，購買者のニーズに近く購買意欲を高めるような商品推薦が可能である．一方，実店舗においては商品の購買以外の情報は，取得と活用の両面における難しさからあまり記録されていない．そのため，購買者がどのような商品に興味を持っており何で悩んでいるのかがわからないという問題がある．こうした問題を解決するために，我々はLiDAR（Light Detection And Ranging）からの点群データとDepth（深度）カメラからの視線情報を用いた購買時における候補商品推定システムを構築し，その初期評価を行った．推定の上では，興味のある商品を購買候補商品とした．評価の結果，購買時の視線は候補商品と強く結びつき，視線の情報は候補商品を推定する上で非常に重要な特徴となり得ることがわかった．一方で，視線の評価を踏まえ点群データを使用した候補商品の予測を行った結果，予測対象の人物を含んだ状態で学習したモデルは49%と12クラス分類であることを考えると一定の精度を出すことができた．一方でLOPOでは精度が最大22%と，モデルの一般化はまだまだ課題が残ることがわかった．今後は，データ数を増やし，モデルをより適したものに変更・改善していく必要がある．

Language：Japanese  

新型コロナウイルスの流行による外出制限を受けて料理動画の需要が高まっているが，料理中に動画を視聴する際，動画再生端末を操作する必要がある．そこで本研究ではユーザの料理工程に合わせて料理動画をループ再生する手法を提案する．ユーザが行っている料理工程を動画内でループ再生することで動画再生端末を操作することなく料理動画を視聴することが可能となる．本稿では，この実現に向けた第一歩として料理音から料理工程を認識する手法について述べる．ユーザの料理音を取得し，既存の料理音データセットで作成した機械学習モデルに入力することで料理工程を認識する．実環境ではテレビの音や BGM が混ざる可能性があるためバックグラウンドノイズの影響について検討した結果も報告する．一般家庭で録音した料理音データを kitchen20 データセットで作成した分類モデルで初期的評価を行った結果，3 種類の料理工程を正解率 0.84 で分類できることを確認した．また料理工程が頻繁に変わらないことを考慮して平滑化処理を行った結果も示す．

Language：Others   Publishing type：Research paper (scientific journal)  

In this paper, we propose a framework called an IoT activity tracker to ensure the safe and secure use of IoT devices around the home. The IoT activity tracker identifies the types of IoT devices and their triggering functions by traffic pattern analysis, and provides information about which IoT devices are running in the home. Also, it allows users to temporarily or permanently block the network of a particular IoT device from their smartphone if the user notices unauthorized activity of the IoT device. Furthermore, the system can be linked to sensor-based behavior recognition. For example, an IoT-based smart lock can automatically switch the communication permissions between those when the user is out and those when the user is at home depending on its status. In this paper, we report the results of a proof of concept experiment on our proposed system, which consists of a router with the proposed function and a cloud server that interfaces with users.

DOI： 10.18494/SAM3901

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

Construction and evaluation of a human-in-the-loop video annotation system for nodding during meetings
Annotation is one of the time consuming manipulation for human to do. In our previous work, we made a nodding recognition model using annotated video data made by an annotator. We find that consumption of annotation time and the work load require for the annotator is huge. In this work we come up with the idea of human-in-the-loop video annotation system, which the machine leaning model create annotation result before the annotator finalize the annotation result.With this system we evaluated how average time consumed in annotation is reduced.

DOI： 10.11517/pjsai.jsai2022.0_3h3os12a03

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

Quantifying a Multi-person Meeting based on Multi-modal Micro-behavior Analysis
In this paper, we present an end-to-end online meeting quantifying system, which can exactly detect and quantify three micro-behavior indicators, speaking, nodding, and smile, for online meeting evaluation. For active speaker detection (ASD), we build a multi-modal neural network framework which consists of audio and video temporal encoders, audio-visual cross-attention mechanism for inter-modality interaction, and a self-attention mechanism to capture long-term speaking evidence. For nodding detection, based on the WHENet framework proposed in the research field of head pose estimation (HPE), we can estimate the head pitch angles as the nodding feature. Then we build a gated recurrent unit (GRU) network with squeeze-and-excitation (SE) module to recognize nodding movement from videos. Finally, we utilize a Haar cascade classifier for smile detection. The experimental results using K-fold Cross Validation show that the F1-score of each detection module achieves 94.9%, 79.67% and 71.19% respectively.

DOI： 10.11517/pjsai.jsai2022.0_1p1gs1004

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

Difficulties in continuous physiological sensing in-the-wild
To realize ubiquitous services based on physiological sensing, it is important to assume that commercially available sensors will be used in daily life. Collecting physiological data from ordinary people for a long period of time in-the-wild is completely different from performing highly accurate physiological sensing using dedicated sensors in a controlled laboratory environment. In this presentation, we will share the various problems we have experienced and clarify the points that need to be considered in order to achieve stable sensing in a real environment.

DOI： 10.11517/pjsai.jsai2022.0_3i3os5a01

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Language：English   Publisher：(NPO)日本脳神経血管内治療学会  

目的:脳血管内治療を行う際に,どこを注視しながら操作を行うかは重要である.アイトラッカーを用いた視線解析により治療経験に応じた脳血管内治療中の視線の違いを検出可能か検証した.方法:被験者は当院で脳血管内治療に従事する治療経験歴の異なる7人の医師とした.治療経験によりExpert群,Trainee群,Novice群に分け,各群での脳血管内治療中の視線の違いを検討した.治療操作には人体血管モデルを利用し,脳動脈瘤に対しbiplane透視下でマイクロカテーテルの動脈瘤内への誘導,コイル挿入を行い,操作中の視線をアイトラッカーで抽出した.結果:マイクロカテーテルの動脈瘤内への誘導では,Expert群,Trainee群が主に正面像を注視して操作しているのに対し,Novice群では側面像とモニター外を見ている時間が長かった.コイル挿入ではExpert群が正面像をメインの画像として注視しサブ画像として側面像としてバランス良く利用していた.一方でTrainee群はほぼ正面像のみを注視し,Novice群はモニター外を見る時間が他の2群に比べ長かった.結論:治療経験による脳血管内治療中の視線の違いがアイトラッカーを用いて解析することが可能であった.今後の脳血管内治療の教育に活用できると期待される.(著者抄録)

Language：Japanese  

Language：Japanese  

Language：Others   Publishing type：Research paper (other academic)   Publisher：IEEE  

DOI： 10.1109/LifeTech53646.2022.9754964

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Other Link： https://dblp.uni-trier.de/db/conf/lifetech/lifetech2022.html#NakagawaAN22

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Others   Publishing type：Research paper (other academic)  

Language：Japanese  

オンラインアンケートにおいて，なるべく楽に早くアンケートタスクを完了しようとする「Satisficing（努力の最小限化）」という態度が調査結果の信頼性を低下させる問題がある．この問題に対し，回答時間が明らかに短い回答を除外するなどの措置が考案されてきたが，回答時間だけでは完全に除外することができない．そこで我々は，回答時間だけでなく，回答中の画面操作を観測することで，より高精度にSatisficingが検出可能になるのではないかという仮説を立てた．世界中で利用されているオープンソースアンケートシステムLimeSurveyの拡張機能として，回答中の画面操作ログ（回答操作ログ）を記録できるプラグインを開発し，本システムを用いてクラウドソーシング上でアンケート実験を行った．実験は，回答者は各自のスマートフォンを用いて，リ
ッカート形式と自由記述形式の質問から成るアンケートに回答してもらうものであり，アンケートの途中にはSatisficingを検出するために考案されている既存の質問を挿入した．1000人に対して回答を依頼し，収集した回答内容および回答操作ログから，
Satisficingを表現する可能性が考えられる特徴量を生成し，特徴量ごとにSatisficingである群とSatisficingでない群の母平均の差の検定を行った．結果として，既存の特徴量であるテキストの文字数，連続同一回答数，中間回答数に加え，開発したプラグインでのみ取得可能なスクロール速度，選択肢の変更回数，リッカート形式の回答時間などに統計的有意差があることを明らかにした．また，スクロール速度やテキストの変更回数に関しては，絶対値では差がない一方で，回答者ごとのベースラインとの差では有意な差が確認できた．
identifier:2189-0374

Language：Japanese  

type:Journal Article
都市部への人口集中と自動車の利用の増加は，交通渋滞を引き起こす原因の一つとなっている．ユーザが交通渋滞を避けるためには，車車間通信(V2V) によるリアルタイムで交通情報が共有できるシステムが有用であると考えられる．しかし，V2V通信の問題点として，建造物や車両の影響により，見通しが取れない環境下での長距離伝送は困難であることが挙げられる．本研究では，低コスト・低消費電力で広域の通信を実現するため，Private LoRaを用いた車車間情報共有のためのデータ転送中継方式を提案する．提案するデータ中継転送方式の有効性を実証実験により検証する． / Population concentration in urban areas and the increase in the use of automobiles are causes of traffic congestion jams. The system sharing traffic information in real-time by vehicle-to-vehicle (V2V) communication is useful for users to avoid traffic jams. However, one of the problems with V2V communication is that it is difficult to transmit over long distances in an environment where visibility is limited due to the influence of buildings and moving vehicles. This paper proposes a data relay scheme for inter-vehicle information sharing using private LoRa with long-range communication. We verify the effectiveness of our proposed data relay scheme through demonstration experiments.
電子情報通信学会 ネットワークシステム研究会（NS）, 2022年3月10日-11日, オンライン開催
source:https://www.ieice.org/ken/paper/20220311TCjl/

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/PerCom53586.2022.9762376

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Other Link： https://dblp.uni-trier.de/db/conf/percom/percom2022.html#AritaNIA22

Language：Others   Publishing type：Research paper (other academic)   Publisher：Springer International Publishing  

To promote the face-to-face communication reduced by COVID-19, we proposed and implemented a context-aware Slack chatbot based on Cyber-Physical sensing that helps colleagues meet more often in the same place. Our system periodically collects the user’s internal context through Slack (cyber sensing) and uses small BLE beacons distributed to colleagues and beacon scanners installed in a laboratory to sense physical attendance (physical sensing). In addition, the system notifies the user of recommended actions, such as lunch or coffee break, depending on the context determined by the Cyber-Physical sensors. We deployed the proposed system in a laboratory environment and conducted an initial experiment for six weeks. Experimental results confirmed that our system can encourage serendipitous face-to-face communication during periods when the frequency of attending school and going to work dropped due to COVID-19. It was also found that in an environment such as a laboratory, where a certain level of trust has already been established, the openness of the collected information can further motivate users to participate in the system.

DOI： 10.1007/978-3-030-98438-0_18

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Other Link： https://dblp.uni-trier.de/db/conf/persuasive/persuasive2022.html#TanakaMNA22

Publishing type：Research paper (international conference proceedings)  

In this study, we propose a behavior change support system that encourages people to use stairs instead of elevators. Using the stairs is not only good for one's health, but nowadays it also plays a role in dense avoidance from the perspective of COVID-19. Although many people are waiting for the elevator, we thought that if we knew how many people were on the elevator, more people could change their behavior. However, there are few buildings where the number of people in the elevator is displayed on each floor. So, we have developed a headcount measurement system in the cargo and a visualization system, those can be easily appended to the current elevator. As a method to measure the number of people in an elevator, we propose a method to detect BLE (Bluetooth Low Energy) signals transmitted from the terminals of elevator users who have installed COCOA, an application for confirming contact with the COVID-19 in Japan, and to measure in real time the number of detected BLE signals with a received signal strength (RSSI) exceeding a certain value. We propose a method to measure the number of BLE signals detected in real time. In this paper, we have designed and developed the system, verified the accuracy of the detection of the continuous operation time and the number of passengers, and estimated the behavior pattern and waiting time of elevator users using the system. The evaluation of the transformation of the decision making of the elevator users brought about by this system is out of the scope of this paper and will be discussed in the future.

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Language：Japanese   Publishing type：Research paper (scientific journal)  

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Language：Japanese   Publisher：[出版社不明]  

Estimating Phone Holding Style through Analysis of Touch Logs
ユーザがどのようにスマートフォンを持ち，どの指で操作しているかという情報（操作形態）が分かれば，様々な新しい応用，たとえば，操作形態に適したUI（ユーザインターフェス）への動的切替えや，ながらコンテキスト（スマートフォン操作時に行われている別行動，歩行，喫煙，食事など）の推定など，への道が拓ける可能性がある．本論文では，タッチ操作によって出力されるログに着目し，タッチやスワイプなどの操作領域や頻度からどのような操作形態かを推定する方法を提案する．そのために，Android上でアプリ横断的にタッチ操作を取得可能なTouchAnalyzerから得られた情報を特徴量とし，機械学習手法により代表的な8通りの操作形態を推定する手法を構築する．16名の実験参加者による評価実験を通じて，提案手法によって操作形態をおおむね90&#37;のF値で推定できることが分かった．
In this paper, we propose a method for estimating the phone holding style that shows hand grasp and finger placement while using a smartphone (e.g., either by both hands or one hand or by putting it on the desk) through analysis of screen touch behavior logs. Knowing the phone holding style in real-time will open the door to various new applications such as dynamically providing a suitable interface (e.g., layouts of buttons) and estimating “side-contexts” like walking, smoking and drinking while using a smartphone. In this paper, focusing on the logs that Android operating systems generate when touch behavior happens, we propose a method to estimate the way of holding a smartphone by area (in the screen) and frequency of touch behavior. We have developed a tool called TouchAnalyzer that identifies touch behavior types like swipe, rotate, pinch, multi-touch, etc. from the device-dependent logs of Android OS. By using the touch behavior type logs that TouchAnalyzer outputs, we have developed an algorithm that can recognizre eight representative phone holding styles. We conducted evaluation experiments with 16 participants and confirmed that the proposed method achives about 90&#37; of F-score in estimating the phone holding style.

DOI： 10.20729/00215710

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Language：Japanese   Publisher：公益社団法人 日本心理学会  

<p>本シンポジウムは，第66回大会から継続して企画されているシンポジウムおよびワークショップ「職場のストレスとメンタルヘルス」の第20回目に相当する。</p><p>わが国では，雇用・労働環境の変化，価値観や働き方の多様化，デジタル化が進展し，社会や働き方が大きく変化している。一方，一人ひとりの労働者も，コロナ禍で働き方の変化に直面する中，働く意義を再考し，より充実した働き方を実現するための方策を模索し始めている。つまり，現在は，社会全体が新しい労働観や働き方へと向かう転換期にあると言える。</p><p>そこで，本シンポジウムでは，これからの職場のメンタルヘルスについて，特に労働観の転換と朗働の実現に注目して検討する。具体的には，（1）朗働/幸福の心理生物的メカニズム，（2）組織内の行動，（3）勤怠データを用いた働き方と休み方，（4）情報技術を用いた行動の測定，の4点を取り上げ，「職業生活においてどのようにウェルビーイングを促進するか？」という課題について検討したい。</p>

DOI： 10.4992/pacjpa.86.0_ss-038

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Publisher：Sensors and Materials  

The rapid proliferation of mobile devices with various sensors has enabled participatory mobile sensing (PMS). Several PMS platforms suffer from open issues including the limited use of their functions to a specific scenario/case and the necessity of technical knowledge for organizers. This paper proposes a novel PMS platform named ParmoSense for easy and flexible data collection. To reduce the burden on both organizers and participants, we employ two novel features. First, essential PMS functions implemented as modules can be easily chosen and combined for sensing in different scenarios. Second, the scenario-based description feature allows organizers to easily and quickly prepare a new instance of PMS and enable people to easily participate in the sensing. It also provides multiple functions to motivate participants for sustainable operation. Through a performance comparison with existing PMS platforms, we confirmed that ParmoSense shows the best cost performance in terms of the workload for preparation and the variety of functions. In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies over four years with ordinary citizens. As the result of a questionnaire survey carried out during the case studies, we confirmed that ParmoSense can be easily operated by ordinary citizens without technical skills.

DOI： 10.18494/SAM3961

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Web of Science

Publisher：CEUR Workshop Proceedings  

In this study, we propose a behavior change support system that encourages people to use stairs instead of elevators. Using the stairs is not only good for one's health, but nowadays it also plays a role in dense avoidance from the perspective of COVID-19. Although many people are waiting for the elevator, we thought that if we knew how many people were on the elevator, more people could change their behavior. However, there are few buildings where the number of people in the elevator is displayed on each floor. So, we have developed a headcount measurement system in the cargo and a visualization system, those can be easily appended to the current elevator. As a method to measure the number of people in an elevator, we propose a method to detect BLE (Bluetooth Low Energy) signals transmitted from the terminals of elevator users who have installed COCOA, an application for confirming contact with the COVID-19 in Japan, and to measure in real time the number of detected BLE signals with a received signal strength (RSSI) exceeding a certain value. We propose a method to measure the number of BLE signals detected in real time. In this paper, we have designed and developed the system, verified the accuracy of the detection of the continuous operation time and the number of passengers, and estimated the behavior pattern and waiting time of elevator users using the system. The evaluation of the transformation of the decision making of the elevator users brought about by this system is out of the scope of this paper and will be discussed in the future.

Scopus

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1145/3486622.3493933

Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2021.3132061

Language：Others  

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-3-030-94822-1_51

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Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU50196.2021.9638882

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU50196.2021.9638884

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-3-030-94822-1_2

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Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU50196.2021.9638886

Language：Others   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Neuroendovascular Therapy  

<p><b>Objective</b>: In various fields, differences in eye-gazing patterns during tasks between experts and novices have been evaluated. The aim of this study was to investigate gazing patterns during neuro-endovascular treatment using an eye-tracking device and assess whether gazing patterns depend on the physician’s experience or skill.</p><p><b>Methods</b>: Seven physicians performed coil embolization for a cerebral aneurysm in a silicone vessel model under biplane X-ray fluoroscopy, and their gazing patterns were recorded using an eye-tracking device. The subjects were divided into three groups according to experience, highly experienced (Expert) group, intermediately experienced (Trainee) group, and less experienced (Novice) group. The duration of fixation on the anterior–posterior (AP) view screen, lateral (LR) view, and out-of-screen were compared between each group.</p><p><b>Results</b>: During microcatheter navigation, the Expert and Trainee groups spent a long time on fixation to AP, while the Novice group split their attention between each location. In coil insertion, the Expert group gazed at both the AP and the LR views with more saccades between screens. In contrast, the Trainee group spent most of their time only on the AP view screen and the Novice group spent longer out-of-screen.</p><p><b>Conclusion</b>: An eye-tracking device can detect different gazing patterns among physicians with several experiences and skill levels of neuroendovascular treatment. Learning the gazing patterns of experts using eye tracking may be a good educational tool for novices and trainees.</p>

DOI： 10.5797/jnet.oa.2021-0053

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PubMed

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Language：Others   Publishing type：Research paper (other academic)   Publisher：Smart Innovation, Systems and Technologies  

In recent years, many organizations have prioritized efforts to detect and treat mental health issues. In particular, office workers are affected by many stressors, and physical and mental exhaustion, which is also a social problem. To improve the psychological situation in the workplace, we need to clarify the cause. In this paper, we conducted a 14-day experiment to collect wristband sensor data as well as behavioral and psychological questionnaire data from about 100 office workers. We developed machine learning models to predict psychological indexes using the data. In addition, we analyzed the correlation between behavior (work content and work environment) and psychological state of office workers to reveal the relationship between their work content, work environment, and behavior. As a result, we showed that multiple psychological indicators of office workers can be predicted with more than 80% accuracy using wearable sensors, behavioral data, and weather data. Furthermore, we found that in the working environment, the time spent in “web conferencing”, “working at home (living room)”, and “break time (work time)’ had a significant effect on the psychological state of office workers.

DOI： 10.1007/978-981-19-0361-8_1

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Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/s21175719

Language：Japanese  

緊迫化する地方自治体の財政への対応として，限られた財源を有効に活用するために，施策成果をより正確に分析し，見直していくEBPM（客観的証拠に基づく政策立案）の推進が求められてきた．しかしながら，現在多くの地域では施策の効果検証や分析まで人手が回らず，また外部委託が必要でお金と時間が足りないなどの課題を抱えており，本格導入には至っていない．そうした状況を打破すべく，国内最大規模の広告位置情報ビッグデータを活用して観光拠点単位で人流の検出と集計を担う政策立案評価システムを開発，自治体職員がコロナ禍の人流抑制などを即時かつ安価に把握で環境を整えることで観光分野におけるEBPMの推進を目指す取り組みを紹介する．

DOI： 10.20729/00212203

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3412841.3441951

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DOI： 10.1109/LifeTech52111.2021.9391950

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DOI： 10.1145/3427477.3429462

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DOI： 10.1145/3427477.3429275

Language：Japanese  

IoT(Internet of Things)と AI(Artificial Intelligence)が急速に進展し，自動運転やスマートホームなども現実のものとなろうとしている.コロナウイルス感染症の広がりにより，リモートワークや遠隔講義が当たり前という時代もあっという間に到来してしまった.本講演では，情報系研究者の立場から，このような最先端の技術が「作業」に与える影響について俯瞰するともに，新しい時代の作業のあり方について述べる.

DOI： 10.32191/jjos.14.003

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DOI： 10.1145/3384419.3430609

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DOI： 10.1109/VTC2020-Fall49728.2020.9348569

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DOI： 10.1145/3384419.3430395

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DOI： 10.1145/3436829.3436868

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DOI： 10.1109/ICIEVicIVPR48672.2020.9306525

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DOI： 10.3390/smartcities3030037

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DOI： 10.1587/transinf.2019EDP7226

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DOI： 10.1109/ISMICT48699.2020.9152614

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DOI： 10.1587/transinf.2019EDP7226

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DOI： 10.3390/smartcities3020013

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DOI： 10.1109/SAS48726.2020.9220076

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DOI： 10.1109/PerComWorkshops48775.2020.9156085

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DOI： 10.1109/PerComWorkshops48775.2020.9156176

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DOI： 10.1109/PerComWorkshops48775.2020.9156248

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DOI： 10.1145/3384657.3385334

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DOI： 10.18494/SAM.2020.2614

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DOI： 10.1002/1348-9585.12132

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DOI： 10.18494/SAM.2020.2614

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Effective Trilateration-based Indoor Localization Method Utilizing Active Control of Lighting Devices

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DOI： 10.1002/1348-9585.12132

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DOI： 10.18494/SAM.2020.2592

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DOI： 10.18494/SAM.2020.2592

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DOI： 10.1007/978-3-030-49289-2_5

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DOI： 10.3390/s19204600

Language：Japanese  

ワンウェイカーシェアリングにおいて車両偏在問題の解決は重要な課題である．これまで，運営会社による効率的な車両移動による解決などが試みられてきたが，コスト面でのデメリットは無視できない．そこで我々は，新たなアプローチとして，潜在的利用者へ車両移動を依頼する手法を提案する．本提案は，依頼トリップの数を少なくしながら，要求トリップの成立数を最大化する問題として設定できる．提案手法の効果を検証するためにシミュレーションによる評価を実施した結果，依頼トリップの受託率が20%であったとしても，要求トリップの受託率を17%向上できることを確認した．また，依頼すべき潜在的利用者について検討するために，実証実験を実施しているパーク24株式会社から提供された利用実績データの分析を行った．その結果，利用行動パターンとして5パターン（常連，2way，分散，局所，1ルート）が存在することを明らかにした．また，そのなかでも依頼すべき利用者の利用パターンが，分散型，局所型であることを考察した．本研究の貢献は，車両偏在問題を解決するための新たなアプローチを提案し，シミュレーションだけでなく，実データの分析と合わせて議論していることである．

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/s19204600

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/SENSORS43011.2019.8956580

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1145/3341162.3345588

Language：Japanese  

Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.2197/ipsjjip.27.614

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1111/coin.12234

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-3-030-22015-0_29

Language：Japanese  

Language：Japanese  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1504/IJAHUC.2019.100779

Language：Japanese  

Language：Japanese  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1155/2019/9320145

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ISMICT.2019.8743900

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/MIPR.2019.00079

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/IIAI-AAI.2018.00025

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/PERCOMW.2019.8730659

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730864

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730861

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730662

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730712

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/PERCOM.2019.8767385

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730674

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730807

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DOI： 10.1109/PERCOMW.2019.8730694

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730833

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/MIPR.2019.00079

Language：Japanese  

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2019.8730659

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU.2018.8653587

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese  

地震などの突発的災害発生時において，避難所への速やかな避難は重要な防災・減災対策の1つである．現状，災害時の避難誘導は，現在地から最寄りの避難所へと誘導するものが一般的である．しかしながら，災害発生時に，大多数の人々が特定エリアに偏って集中するような状況下においては，単に最寄りの避難所に誘導するだけでは避難所の収容数の超過を招き，別の避難所への移動を余儀なくされる．結果として，被災者は空きのある避難所へたどり着くまでたらい回しにされ，避難が大幅に遅れてしまう問題が生じる．本論文では，この問題を解決するために，多くの人々が活動している地域を対象として，被災者全体の避難時間を削減することを目的とする避難所決定手法を提案する．具体的には，被災時における被災者の位置情報と各避難所の収容可能数に基づいて，被災者全体の避難時間が短くなるように避難所を決定する．提案手法の有効性を評価するために，京都の祇園祭を想定したシミュレーション環境を構築し，一般的な避難所決定手法である最短経路選択およびランダム選択を適用した場合と提案手法を適用した場合の避難時間を比較した．その結果，提案手法を用いた場合に，最短経路選択と比較して平均避難時間を35.9%削減できることを明らかにした．また，提案手法に従う人が全体の20%であっても，平均避難時間を8.1%削減できることを確認した．この結果から，たとえ一部の人であっても，提案手法に従って避難することによって，全体の避難時間を短縮でき，防災・減災に寄与できることが分かった．

Language：Japanese  

Google MapsやOpen Street Map（OSM）では，1つの地図に複数の地理情報（POI：Point of Interest）を集めて独自の地図を作成可能にする機能が提供されている．この機能は，マイマップ（Google Maps）あるいはuMap（OSM）と呼ばれており，ニッチな情報を集めた地図を含めて，これまでに多種多様な地図が作成されている．しかしながら，これらの地図は，含まれる情報が不十分であることが多く，キーワードを用いて，目的に合致するマイマップを検索することが困難である．本論文では，マイマップ検索の利便性を向上させるため，これまでに存在しなかったマイマップの検索法として，含まれるPOIのカテゴリ比率を特徴とした検索システムを提案する．また，提案システムを実現するにあたり，既存マイマップの問題である情報不足を補うため，1）不完全なPOI情報からPOI情報データベース内のPOIを同定する手法と2）POI名称から属するカテゴリを推定する手法，から構成されるPOI情報補完手法を提案する．インターネット上に公開されているマイマップ5,526件を収集し，その中に含まれるPOI 46,996件について，POI情報の同定精度やカテゴリの推定精度を評価した結果，58.82&#37;を同定可能かつ70.3&#37;のカテゴリを正しく推定できることを明らかにした．さらに，プロトタイプシステムを実装し，10名の被験者を用いて検索の利便性について評価したところ，含まれるPOIカテゴリ比率に基づく検索により，所望するマイマップに近いマップを一定の精度で検索可能であることを確認した．

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/IWQoS.2018.8624127

Language：Others  

Language：Japanese  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2197/ipsjjip.27.614

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1504/IJAHUC.2019.100779

Language：English   Publishing type：Research paper (scientific journal)  

In recent years, many types of research and developments on behavior change have been conducted. The purpose of behavior change is to improve people's lifestyle pattern or to maintain the improvement for a long time with the aim to achieve a goal such as promoting health condition improvement. To achieve the foundation of a new lifestyle, it is necessary to recognize the daily life patterns of users and give triggers for behavior change to users in their daily life. To realize this, in our research, we develop an interactive signage which is able to identify and actively talk to the passing user and try to induce behavior change by sending visual and auditory stimulation. Then, we record users' reactions and upload them to the server. In this paper, we report the investigation result on users' reactions and feelings to the developed interactive signage. As a survey experiment, we set up four interactive signs on a floor of our university and asked 15 participants to carry a name tag with a Bluetooth Low Energy beacon during their daily life. Five kinds of tasks based on dialogue scenarios are posted to the approaching participants. Participants can respond to these tasks through a touchscreen. The period of the experiment was three weeks. To get the data in an ideal environment, during the first week, we asked all the participants to respond to the utterance from the interactive signage whenever they hear the voice message. During the next two weeks, participants were not asked to respond to the task definitely to get the data in the real environment. The result of the experiment showed that our proposed interactive signage could induce behavior change effectively. Based on the result of experiment, we updated our interactive signage system by adding response time (the time passed from showing contents until user respond), record function, and voice feedback function. Furthermore, to collect the data of response time that is considered as a part of users' reactions, we conducted an additional experiment with the same participants in previous experiment (except for one missing participant) for one week after updating the system. In the additional experiment, the participants were not asked to respond to the utterance definitely. As a result, it is shown that the behavior change by the proposed signage is still effectively induced. We also analyzed the relationship between the day passed and the response rate of each task type. The result shows that the number of ignorance of personal task and check task does not rise even as the time passes.

DOI： 10.1111/coin.12234

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1155/2019/9320145

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/SEC.2018.00022

Language：Japanese  

【目的】本研究では，医師の臨床データベースの構築作業の負担軽減のために，カルテテキストを臨床データベース化する自動要約システムの開発を行う． 【材料・手法】循環器分野を対象に，循環医が臨床データベースを構築する際に頻用される11項目 (性別，年齢，体重，身長，HbA1c，CRP，血圧，薬剤の使用歴，糖尿病の有無，喫煙の有無，飲酒の有無)に関して，ルールベースと機械学習を組み合わせることにより，自動で抽出を行う． 【結果・考察】機械学習による自動抽出では，高い精度で情報を抽出することができる可能性が示唆された．

DOI： 10.11517/jsaisigtwo.2018.aimed-006_01

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/s18113978

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/s18113978

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480348

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267687

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267526

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267504

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU.2018.8653587

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/SEC.2018.00022

Language：Others   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267667

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267624

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480395

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267624

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267526

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267504

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480211

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267687

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480256

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3267305.3267667

Language：Japanese  

Language：Japanese  

Language：Japanese  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3229710.3229727

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3229710.3229727

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/SMARTCOMP.2018.00036

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1155/2018/2625195

Language：Japanese  

Language：English   Publishing type：Research paper (scientific journal)  

Heart Rate Prediction for Easy Waking Route Planning

Language：Japanese  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/SMARTCOMP.2018.00036

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.3233/978-1-61499-874-7-244

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/IWQoS.2018.8624127

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.3233/978-1-61499-874-7-196

Language：Japanese  

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Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-981-13-9443-0_15

Language：Japanese  

Language：Japanese  

Language：Japanese  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU.2017.8330069

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.23919/ICMU.2017.8330104

Language：English  

DOI： 10.4018/978-1-5225-5475-2.ch022

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480395

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480211

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480256

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2018.8480348

Language：Japanese  

Language：Japanese  

Language：Japanese  

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Language：English   Publishing type：Research paper (other academic)  

Development of Automatic Daily Report Generation System for Supporting Daily Works on Carers

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ICCE.2018.8326275

Language：Japanese  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/978-3-319-90740-6_13

Language：Japanese  

Language：Japanese  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-3-319-98551-0_11

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/978-3-319-70636-8_3

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/IIAI-AAI.2018.00025

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2018.2793844

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ICCE.2018.8326275

Language：Japanese  

飲食店用不動産店舗の賃料は，ベテラン営業職員の勘や経験，貸主の意向などの暗黙知によって決定されている．しかしながら，従来手法では，賃料を決定している具体的な根拠が示せない問題がある．また，ベテラン営業職員から経験の浅い新人営業職員への知識継承にも課題がある．本研究では，これらの問題を解決するために，ベテラン営業職員の暗黙知に基づいた賃料推定概念モデルの提案，および暗黙知の指標化と機械学習を用いた推定モデルを提案している．複数の機械学習手法に対して検証を行った結果，ランダムフォレストを用いたときが最も優れており，決定係数が0.738と比較手法の中で最大になることを確認した．また，平均二乗誤差（RMSE）についても同様に確認した結果，提案した3つの指標を用いてランダムフォレストを用いたときにRMSEが最小になったことから，提案手法の有効性を確認した．

Language：English   Publishing type：Research paper (scientific journal)  

Language：English  

DOI： 10.1007/978-3-319-70636-8_3

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2018.2793844

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1007/978-3-319-90740-6_13

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1155/2018/2625195

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/978-3-319-98551-0_11

Language：Japanese  

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/ICPPW.2017.14

Language：Japanese  

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Language：English   Publishing type：Research paper (scientific journal)  

Smartphone-based Food Weight and Calorie Estimation Method for Effective Food Journaling
World health statistics about overweight and obesity show that overweight, obesity and diet-related diseases still remain major health risks. According to the World Health Organization, most of the world's population live in countries where overweight and obesity kill more people than underweight. Recently, many studies have been driven by the motivation of elaborating the &ldquo;ideal&rdquo; solutions to prevent and/or monitor overweight, obesity and diet-related diseases, and to encourage healthy diet and lifestyle. In this work, we present our idea to automatically measure food weight and calories, from food photo using ordinary chopsticks as a measurement reference. The analysis of the obtained results show that the use of near-by eating utensils combined with computer vision techniques is a great and exploitable approach to ubiquitously help in diet assessment and obesity treatment.

DOI： 10.9746/jcmsi.10.360

Language：English   Publishing type：Research paper (scientific journal)  

Implementation and Evaluation of Analog-PIR-sensor-based Activity Recognition
These days, smart home applications such as concierge service for residents, home appliance control, and so on are attracting attention. To realize these applications, we need a system which recognizes various human activities accurately with a low cost device. There are many studies on the activity recognition in a smart home. We also have proposed an activity recognition technique in a smart home by utilizing digital-output-PIR (passive infrared) sensors, door sensors, and power meters. However, the study has an unsolved issue: we cannot distinguish similar activities happening at the same place, for example, &ldquo;eating&rdquo; and &ldquo;reading&rdquo; while sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the different activities in the same place. Our technique recognizes user's activity by utilizing machine learning with frequency components of the sensor's output as features. However, because the number of features used in ALPAS is 1000 for each analog PIR-sensor, a large capacity memory is required. To reduce the number of features, we select a part of the sensing data. We call the starting point of the selected data as starting frequency (SF) and ending part as ending frequency (EF). We searched SF and EF using a grid search, and evaluated the recognition accuracy. We evaluated the proposed technique in a smarthome testbed. In the evaluation, five participants performed four different activities while sitting on a sofa. As a result, we achieved F-Measure: 63.9&#37; when the EF is 1.4Hz, and F-Measure: 50&#37; or lower when the SF is 9.9Hz or higher.

DOI： 10.9746/jcmsi.10.385

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1145/3123024.3129267

Language：English   Publishing type：Research paper (other academic)  

A System for Collecting and Curating Sightseeing Information toward Satisfactory Tour Plan Creation

Language：English   Publishing type：Research paper (other academic)  

Recommending Optimal Route for Walking Support based Heart Rate Prediction

Language：Japanese  

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Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/AINA.2017.33

Language：Japanese  

マラソンや自転車競技のようなレース競技において，競技者を応援するための観客向け位置情報共有サービスが始まっている．しかしながら，競技者の位置を得るために，競技者がスマートフォンを持って競技に参加する必要があるという問題がある．そこで本研究では，観客や先導バイクのスマートフォンを位置捕捉のためのインフラとして利用する参加型位置共有システムを提案する．提案システムでは，競技者は，小型BLEビーコンを装着するだけでよく，そのビーコンから発信された電波を受信した観客のスマートフォンの位置情報を競技者の位置情報として共有する．高速に移動（自転車競技における自転車の速度は30km/h～80km/h）するビーコンの位置を観客のスマートフォンで計測するシステムは，先行研究がないため，ビーコンやスマートフォンアプリを開発したうえで，実験を重ねることで，高速で移動するビーコンがスマートフォンの前を通過する場合の受信強度，最大受信距離，GPSから得られる位置情報との差異，先導バイクで受信する際の電波遮蔽，地形と電波受信状況の相関関係，ビーコン側の電波強度や送信周期，など実現に必要なパラメータを明らかにした．さらに，実際のレースに提案システムを導入し，その有効性と可用性を確認した

Language：Japanese  

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Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/PERCOMW.2017.7917620

Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1109/AINA.2017.34

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DOI： 10.1109/WAINA.2017.43

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DOI： 10.1109/PERCOMW.2017.7917587

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DOI： 10.1109/PERCOMW.2017.7917603

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DOI： 10.1145/3055031.3055068

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DOI： 10.1016/j.comcom.2016.12.003

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.comcom.2016.12.003

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近年、Google Maps等の地図サービスの普及に伴い、ユーザが興味のある場所(POI：Point of Interest)をまとめたマイマップの共有が行われている。2014年には、マイマップをフリーキーワードで検索可能なサービスが開始している。しかし、大半のマイマップに登録されているPOIにはカテゴリ情報がなく、どのようなカテゴリのPOIをまとめマイマップか分からない現状にある。そこで、本稿ではPOIの名称からカテゴリを推定する方法を提案する。提案手法では、Foursquareから得られるPOIの名称とカテゴリの関係を機械学習させ、POIのカテゴリを推定する。評価の結果、25種類のカテゴリを55.6%の精度で推定可能である。また、上位5種の平均精度は82.0%、上位10種の平均精度は72.2%である。

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近年，デスクワークの増加により，椅子に座って作業するオフィスワーカーが増加している．オフィスワーカーの生産性や作業効率の向上のためには，ワークプレイスにおいて快適に作業に取り組めるようにすることが重要である．本研究では，椅子に座って作業することが多いオフィスワーカーに着目し，着座姿勢によるワークプレイスの高機能化を提案する．高機能化のためにモーションセンサを取り付けたオフィスチェアによって着座姿勢を推定し，その結果から長時間同じ姿勢で座っているときは休憩の通知，離床時にはPCの画面を消灯，椅子に深くもたれたときはコーヒーを自動で淹れるなどを行う．

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ユビキタス情報技術の発展によって，省エネ家電制御や高齢者見守りシステム，コンシェルジュ機能など生活を支援するサービスへの応用が期待されている．これらのサービスの実現には，多種多様な人間の生活行動を高精度，かつ低コストで認識することが重要である．スマートホームにおける行動推定のアプローチは多数存在するが，(1)カメラやマイクを使用するためにプライバシを侵害する，(2)導入・運用コストが高い，(3)ユーザへの装着負担が大きい，(4)電源・データ収集のための配線が必要，(5)認識できる行動の種類が少ない，(6)認識の精度が低いという6つの課題が存在する．本論文では，これら6つの課題をすべて解決することを目指した生活行動認識手法を提案する．課題(1)～(4)を解決するために，エナジハーベスト焦電型赤外線センサ・ドア開閉センサおよび家電に付帯した消費電力センサのみを活用したユーザの行動推定を実現する．このエナジハーベスト焦電型赤外線センサ・ドア開閉センサは太陽光パネルと大容量キャパシタを内蔵しており，これらから取得した電力を用いて半永続的な運用が可能である．また，課題(5)，(6)を解決するために，(i)教師データに対する効果的な特徴量の選定，(ii)適切な行動学習モデルの構築，(iii)焦電型赤外線センサの不感帯に対する補完処理を行っている．提案手法の有用性を評価するため，筆者らの大学内設置したスマートホーム設備（1LDK）で，計5名の被験者に2～3日ずつ生活してもらい，計13日間評価実験を行った，評価実験の結果，F-measure: 68.6%でユーザの行動を推定した．

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DOI： 10.1527/tjsai.WII-O

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DOI： 10.1155/2017/6308302

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DOI： 10.1145/3012709.3016072

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DOI： 10.1145/3012709.3016072

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DOI： 10.1109/ICDCSW.2016.37

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DOI： 10.1109/ICMU.2016.7742089

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DOI： 10.1109/ICMU.2016.7742087

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DOI： 10.1109/ICMU.2016.7742096

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DOI： 10.1145/3004010.3006380

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DOI： 10.1145/3004010.3006378

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DOI： 10.1145/3004010.3004036

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DOI： 10.1109/IPIN.2016.7743623

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DOI： 10.1109/ICMU.2016.7742088

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DOI： 10.1109/IPIN.2016.7743667

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Sensing thermal stress at office workplaces

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DOI： 10.1145/2968219.2971397

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Smart Experiment Platform for Wearable Computing

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DOI： 10.1145/296219.2968441

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DOI： 10.1145/2968219.2968268

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DOI： 10.1145/2968219.2971399

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DOI： 10.1109/ICPPW.2016.18

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DOI： 10.1109/ICDCS.2016.101

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DOI： 10.1109/IIAI-AAI.2016.110

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Project IS^3 is the leading-edge project in Nara Institute of Science and Technology, a national graduate school in Japan, started from February 2016. This project aims to utilize a human's behavior change for solving the social problems and maintaining our society sustainably. In order to cause the behavior change intentionally, various information technologies are required. In this paper, we explain the concept and goal of our project and figure out what we will do in this project.

DOI： 10.1109/IIAI-AAI.2016.97

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DOI： 10.1109/ICDCS.2016.85

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DOI： 10.4018/978-1-5225-5475-2.ch022

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Priority medical image delivery using DTN for healthcare workers in volcanic emergency