Updated on 2024/10/08

Information

 

写真a

 
CHOI HYUCKJIN
 
Organization
Faculty of Information Science and Electrical Engineering Department of Advanced Information Technology Assistant Professor
Title
Assistant Professor
Contact information
メールアドレス
External link

Research Areas

  • Informatics / Intelligent informatics

  • Informatics / Information network

Degree

  • Doctor of Engineering

Research History

  • Kyushu University Faculty of Information Science and Electrical Engineering Assistant Professor

    2022.10 - Present

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    Country:Japan

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  • Kyushu University Faculty of Information Science and Electrical Engineering Specially Appointed Assistant Professor

    2022.7 - 2022.9

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    Country:Japan

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  • Nara Institute of Science and Technology Graduate School of Science and Technology Researcher

    2022.4 - 2022.6

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    Country:Japan

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  • 奈良先端科学技術大学院大学・研究員

Education

  • Nara Institute of Science and Technology   Graduate School of Science and Technology   Division of Information Science

    2019.4 - 2022.6

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    Country: Japan

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Research Interests・Research Keywords

  • Research theme:Human Activity Recognition

    Keyword:Human Activity Recognition

    Research period: 2024

  • Research theme:Wireless Sensing

    Keyword:Wireless Sensing

    Research period: 2024

  • Research theme:Machine Learning

    Keyword:Machine Learning

    Research period: 2024

  • Research theme:Signal Processing

    Keyword:Signal Processing

    Research period: 2024

  • Research theme:Ubiquitous Computing

    Keyword:Ubiquitous Computing

    Research period: 2024

  • Research theme:Internet of Things

    Keyword:Internet of Things

    Research period: 2024

Awards

  • Best Demonstration Runner-up Award

    2023.11   The 13th International Conference on the Internet of Things (IoT 2023)  

  • Best Demonstration Runner-up Award

    2023.11   The 13th International Conference on the Internet of Things (IoT 2023)   Counting Nods from Chair Rocking

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

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  • 奨励賞

    2023.10   第31回マルチメディア通信と分散処理ワークショップ(DPS Workshop 2023)   椅子の揺れに基づく頷き認識システムの設計と構築

  • 奨励賞

    2023.10   第31回マルチメディア通信と分散処理ワークショップ(DPS Workshop 2023)   椅子の揺れに基づく頷き認識システムの設計と構築

    林田 宗樹, 中村 優吾, 崔 赫秦, 荒川 豊

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  • 第38回電気通信普及財団賞(テレコム学際研究学生賞)入賞

    2023.3   公益財団法人電気通信普及財団  

  • 38th Student Award of Telecom Interdisciplinary Research awarded by the Telecommunication Adavancement Foundation

    2023.3   The Telecommunications Advancement Foundation   Wi-CaL: WiFi Sensing and Machine Learning based Device-Free Crowd Counting and Localization

    Hyuckjin Choi

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Papers

  • Privacy-aware Quantitative Measurement of Psychological State in Meetings based on Non-verbal Cues

    Hayashida, T; Nakamura, Y; Choi, H; Arakawa, Y

    2024 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS, PERCOM WORKSHOPS   433 - 436   2024   ISSN:2836-5348 ISBN:979-8-3503-0437-4

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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

    Web of Science

    Scopus

  • Poster: Desk Activity Recognition Using On-desk Low-cost WiFi Transceiver

    Choi, H; Nakamura, Y; Fukushima, S; Arakawa, Y

    PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024   702 - 703   2024   ISBN:979-8-4007-0581-6

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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

    Web of Science

    Scopus

  • Poster: Annotation Assist System Using Backscatter Tags for WiFi CSI-based Indoor Activity Recognition

    Kai, K; Choi, H; Nakamura, Y; Arakawa, Y

    PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024   680 - 681   2024   ISBN:979-8-4007-0581-6

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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

    Web of Science

    Scopus

  • Design and Implementation of Persuasive Public Wi-Fi to Derive Prosocial Network Usage

    Eguchi, N; Choi, H; Nakamura, Y; Fukushima, S; Arakawa, Y

    38TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN 2024   351 - 356   2024   ISSN:19767684 ISBN:979-8-3503-3095-3

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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

    Web of Science

    Scopus

  • Bilatangulation: A Novel Measurement Error Compensation Method for Wi-Fi Indoor Positioning With Two Anchors

    Lee, CH; Choi, H; Arakawa, Y; Kim, DH; Kim, JD

    IEEE ACCESS   12   128652 - 128661   2024   ISSN:2169-3536

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    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

    Web of Science

    Scopus

  • Counting Nods from Chair Rocking

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

    ACM International Conference Proceeding Series   208 - 210   2023.11   ISBN:9798400708541

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    Publishing type:Research paper (international conference proceedings)  

    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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  • WatchLogger: Keyboard Typing Words Recognition Based on Smartwatch Reviewed

    Gangkai Li, Yutaka Arakawa, Yugo Nakamura, Hyuckjin Choi, Shogo Fukushima, Wei Wang

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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    Language:Others  

  • Design and Implementation of Nodding Recognition System Based on Chair Sway Reviewed

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • Investigation on Deployment Pattern of Wi-Fi Transceivers for CSI-Based Indoor Localization and Activity Recognition Reviewed

    Kiichiro Kai, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • Location-Independent Doppler Sensing System for Device-Free Daily Living Activity Recognition Reviewed

    Shinya Misaki, Makoto Yoshida, Hyuckjin Choi, Tomokazu Matsui, Manato Fujimoto, Keiichi Yasumoto

    IEEE Access   11   127754 - 127768   2023.11   ISSN:2169-3536

  • Tracking On-Desk Gestures Based on WiFi CSI on Low-Cost Microcontroller Reviewed

    Marwa Bastwesy, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • Design and Implementation of Nodding Recognition System Based on Chair Sway Reviewed

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • WatchLogger: Keyboard Typing Words Recognition Based on Smartwatch Reviewed

    Gangkai Li, Yutaka Arakawa, Yugo Nakamura, Hyuckjin Choi, Shogo Fukushima, Wei Wang

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • Tracking On-Desk Gestures Based on WiFi CSI on Low-Cost Microcontroller Reviewed

    Marwa Bastwesy, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • Investigation on Deployment Pattern of Wi-Fi Transceivers for CSI-Based Indoor Localization and Activity Recognition Reviewed

    Kiichiro Kai, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023.11

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  • HeMoFi4Q: Morse Communication Based on Wi-Fi and Head Motion for Quadriplegia with Environmental Robustness Reviewed

    Marwa Bastwesy, Hyuckjin Choi, Yutaka Arakawa

    IEEE Access   11   116384 - 116397   2023.10   ISSN:2169-3536

  • Wi-Nod: Head Nodding Recognition by Wi-Fi CSI Toward Communicative Support for Quadriplegics

    Marwa Bastwesy, Kiichiro Kai, Hyuckjin Choi, Shigemi Ishida, Yutaka Arakawa

    IEEE Wireless Communications and Networking Conference, WCNC   2023-March   2023.3   ISSN:1525-3511 ISBN:978-1-6654-9122-8

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

    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% recognition accuracy that reveals the feasibility of Wi-Nod system for real-life deployment.

    DOI: 10.1109/WCNC55385.2023.10118666

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  • WatchLogger: Keyboard Typing Words Recognition Based on Smartwatch

    Li G., Arakawa Y., Nakamura Y., Choi H., Fukushima S., Wang W.

    2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023   2023   ISBN:9784907626525

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    Publisher:2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

    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

    Scopus

  • WatchLogger: Keyboard Typing Words Recognition based on Smartwatch

    Li, GK; Arakawa, Y; Nakamura, Y; Choi, H; Fukushima, S; Wang, W

    2023 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU   2023   ISBN:978-4-907626-52-5

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  • Tracking On-Desk Gestures Based on Wi-Fi CSI on Low-Cost Microcontroller

    Bastwesy M.R.M., Choi H., Arakawa Y.

    2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023   2023   ISBN:9784907626525

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    Publisher:2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

    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

    Scopus

  • Tracking On-Desk Gestures Based on Wi-Fi CSI on Low-Cost Microcontroller

    Bastwesy, MRM; Choi, H; Arakawa, Y

    2023 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU   2023   ISBN:978-4-907626-52-5

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  • Investigation on Deployment Pattern of Wi-Fi Transceivers for CSI-Based Indoor Localization and Activity Recognition

    Kai K., Choi H., Arakawa Y.

    2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023   2023   ISBN:9784907626525

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    Publisher:2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

    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

    Scopus

  • Investigation on Deployment Pattern of Wi-Fi Transceivers for CSI-based Indoor Localization and Activity Recognition

    Kai, K; Choi, H; Arakawa, Y

    2023 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU   2023   ISBN:978-4-907626-52-5

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  • Design and Implementation of Nodding Recognition System Based on Chair Sway

    Hayashida T., Nakamura Y., Choi H., Arakawa Y.

    2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023   2023   ISBN:9784907626525

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    Publisher:2023 14th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2023  

    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

    Scopus

  • Design and Implementation of Nodding Recognition System Based on Chair Sway

    Hayashida, T; Nakamura, Y; Choi, H; Arakawa, Y

    2023 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU   2023   ISBN:978-4-907626-52-5

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  • Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization

    Hyuckjin Choi, Manato Fujimoto, Tomokazu Matsui, Shinya Misaki, Keiichi Yasumoto

    IEEE Access   10   24395 - 24410   2022.3   eISSN:2169-3536

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

    Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct Wi-CaL, a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).

    DOI: 10.1109/ACCESS.2022.3155812

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  • Simultaneous Crowd Estimation in Counting and Localization Using WiFi CSI

    Hyuckjin Choi, Tomokazu Matsui, Shinya Misaki, Atsushi Miyaji, Manato Fujimoto, Keiichi Yasumoto

    2021 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2021   2021.11

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

    DOI: 10.1109/IPIN51156.2021.9662572

  • Analysis on Nursing Care Activity Related Stress Level for Reduction of Caregiving Workload

    Atsushi Miyaji, Tomokazu Matsui, Zhihua Zhang, Hyuckjin Choi, Manato Fujimoto, Keiichi Yasumoto

    ACM International Conference Proceeding Series   2021.8

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

    DOI: 10.1145/3458744.3473346

  • Non-contact Person Identification by Piezoelectric-Based Gait Vibration Sensing

    Keisuke Umakoshi, Tomokazu Matsui, Makoto Yoshida, Hyuckjin Choi, Manato Fujimoto, Hirohiko Suwa, Keiichi Yasumoto

    Lecture Notes in Networks and Systems   225 LNNS   745 - 757   2021.5

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

    DOI: 10.1007/978-3-030-75100-5_63

  • Simultaneous Crowd Counting and Localization by WiFi CSI

    Hyuckjin Choi, Tomokazu Matsui, Manato Fujimoto, Keiichi Yasumoto

    ACM International Conference Proceeding Series   239 - 240   2021.1

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

    DOI: 10.1145/3427796.3430000

  • Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors

    Shinya Misaki, Keisuke Umakoshi, Tomokazu Matsui, Hyuckjin Choi, Manato Fujimoto, Keiichi Yasumoto

    ACM International Conference Proceeding Series   169 - 174   2021.1

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

    DOI: 10.1145/3427477.3429463

  • Fishing activity sensing and visualization system using sensor-equipped fishing rod: Demo abstract

    Shuichi Fukuda, Hyuckjin Choi, Yuki Matsuda, Keiichi Yasumoto

    SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems   615 - 616   2020.11

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

    DOI: 10.1145/3384419.3430447

  • RSS bias compensation in BLE beacon based positioning system

    Hyuckjin Choi, Heetae Jin, Suk Chan Kim

    International Conference on Ubiquitous and Future Networks, ICUFN   494 - 497   2017.7

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

    DOI: 10.1109/ICUFN.2017.7993833

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Presentations

  • Wi-Fi CSI を用いた住居全体の位置・活動推定に適した送受信機設置パターンの調査

    甲斐 貴一朗, 崔 赫秦, 荒川 豊

    第107回モバイルコンピューティングと新社会システム研究会 (MBL)  2023.5 

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    Language:Others  

    Country:Japan  

MISC

  • 向社会的なネットワーク利用を説得的に促す公衆Wi-Fiの設計と実装

    江口 直輝, 崔 赫秦, 中村 優吾, 福嶋 政期, 荒川 豊

    情報処理学会IoT行動変容学研究グループ 第6回研究会 (BTI6)   2023.12

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  • 向社会的なネットワーク利用を説得的に促す公衆Wi-Fiの設計と実装

    江口 直輝, 崔 赫秦, 中村 優吾, 福嶋 政期, 荒川 豊

    情報処理学会IoT行動変容学研究グループ 第6回研究会 (BTI6)   2023.12

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  • 椅子の揺れに基づく頷き認識システムの設計と構築

    林田 宗樹, 中村 優吾, 崔 赫秦, 荒川 豊

    第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023)   2023.10

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    Language:Others  

  • 椅子の揺れに基づく頷き認識システムの設計と構築

    林田 宗樹, 中村 優吾, 崔 赫秦, 荒川 豊

    第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023)   2023.10

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  • 四肢麻痺患者の意思疎通サポートに向けた無線センシングによる頭部モーション推定

    M. Bastwesy, 甲斐 貴一朗, 崔 赫秦, 荒川 豊

    マルチメディア、分散、協調とモバイル (DICOMO 2023) シンポジウム   2023.7

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    Language:Others  

  • DNSクエリログを活用した国籍判定手法による多言語デジタルサイネージシステムの提案

    江口 直輝, 崔 赫秦, 中村 優吾, 福嶋 政期, 荒川 豊

    マルチメディア、分散、協調とモバイル (DICOMO 2023) シンポジウム   2023.7

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    Language:Others  

  • 四肢麻痺患者の意思疎通サポートに向けた無線センシングによる頭部モーション推定

    M. Bastwesy, 甲斐 貴一朗, 崔 赫秦, 荒川 豊

    マルチメディア、分散、協調とモバイル (DICOMO 2023) シンポジウム   2023.7

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  • DNSクエリログを活用した国籍判定手法による多言語デジタルサイネージシステムの提案

    江口 直輝, 崔 赫秦, 中村 優吾, 福嶋 政期, 荒川 豊

    マルチメディア、分散、協調とモバイル (DICOMO 2023) シンポジウム   2023.7

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  • Wi-Fi CSI を用いた住居全体の位置・活動推定に適した送受信機設置パターンの調査

    甲斐 貴一朗, 崔 赫秦, 荒川 豊

    第107回モバイルコンピューティングと新社会システム研究会 (MBL)   2023.5

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Professional Memberships

Committee Memberships

  • International Conference on Maritime IT Convergence (ICMIC)   Technical Program Committee  

    2023.3 - 2023.8   

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    Committee type:Academic society

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