2024/10/08 更新

お知らせ

 

写真a

 
崔 赫秦
CHOI HYUCKJIN
所属
システム情報科学研究院 情報知能工学部門 助教
職名
助教
連絡先
メールアドレス
外部リンク

研究分野

  • 情報通信 / 知能情報学

  • 情報通信 / 情報ネットワーク

学位

  • 博士 (工学)

経歴

  • 九州大学 大学院システム情報科学研究院 助教

    2022年10月 - 現在

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    国名:日本国

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  • 九州大学 大学院システム情報科学研究院 特任助教

    2022年7月 - 2022年9月

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    国名:日本国

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  • 奈良先端科学技術大学院大学 先端科学技術研究科 研究員

    2022年4月 - 2022年6月

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    国名:日本国

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  • 奈良先端科学技術大学院大学・研究員

学歴

  • 奈良先端科学技術大学院大学   先端科学技術研究科   情報科学領域

    2019年4月 - 2022年6月

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    国名: 日本国

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研究テーマ・研究キーワード

  • 研究テーマ:行動認識

    研究キーワード:行動認識

    研究期間: 2024年

  • 研究テーマ:無線センシング

    研究キーワード:無線センシング

    研究期間: 2024年

  • 研究テーマ:機械学習

    研究キーワード:機械学習

    研究期間: 2024年

  • 研究テーマ:信号処理

    研究キーワード:信号処理

    研究期間: 2024年

  • 研究テーマ:ユビキタスコンピューティング

    研究キーワード:ユビキタスコンピューティング

    研究期間: 2024年

  • 研究テーマ:IoT

    研究キーワード:IoT

    研究期間: 2024年

受賞

  • Best Demonstration Runner-up Award

    2023年11月   The 13th International Conference on the Internet of Things (IoT 2023)  

  • Best Demonstration Runner-up Award

    2023年11月   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年10月   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年10月   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年3月   公益財団法人電気通信普及財団  

  • 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年3月   公益財団法人電気通信普及財団  

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▼全件表示

論文

  • 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年   ISSN:19767684 ISBN:979-8-3503-3095-3

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    出版者・発行元: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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  • 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年   ISSN:2169-3536

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    出版者・発行元: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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  • 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年   ISBN:979-8-4007-0581-6

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    出版者・発行元: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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  • 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年   ISSN:2836-5348 ISBN:979-8-3503-0437-4

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    出版者・発行元: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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  • 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年   ISBN:979-8-4007-0581-6

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    出版者・発行元: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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  • Counting Nods from Chair Rocking

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

    ACM International Conference Proceeding Series   208 - 210   2023年11月   ISBN:9798400708541

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    掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:ACM International Conference Proceeding Series  

    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 査読

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

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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    記述言語:その他  

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

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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    記述言語:その他  

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

    Kiichiro Kai, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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    記述言語:その他  

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

    Misaki, S; Yoshida, M; Choi, H; Matsui, T; Fujimoto, M; Yasumoto, K

    IEEE ACCESS   11   127754 - 127768   2023年11月   ISSN:2169-3536

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    記述言語:その他   出版者・発行元:IEEE Access  

    The recent advancements in sensing technology have opened up the possibilities for various services that support daily life, such as energy-saving home appliance control. To realize such services, accurate and cost-effective daily living activity recognition in a wide range is essential. To actualize such a system, it is imperative to address the following requirements: the acquisition of sensors entails very high costs (Issue 1), it is hard to achieve precise recognition for location-independent activities like reading a book (Issue 2), a burden of wearing devices from the perspective of residents (Issue 3), and the preservation of residents' privacy is compromised by using image data from the camera (Issue 4). In this paper, we propose a method for recognizing daily living activities utilizing Doppler sensors in a relatively longer detection range than other motion detection sensors that can be used for dynamic objects. We assess the proposed system by optimizing recognition accuracy, evaluating ensemble methods, and examining sensor reduction's impact. In the first assessment, the logistic regression achieved the highest accuracy of 65.99% in the leave-one-person-out cross-validation. The second assessment revealed an accuracy of 59.39% for the parallel activity recognition method and 57.24% for the joint recognition method of location and activity. In the third assessment, logistic regression achieved a recognition accuracy of 65.26% when four sensor nodes were used: two sensors were placed on both sides of a participant, another was diagonally behind the participant, and the other was installed on the ceiling.

    DOI: 10.1109/ACCESS.2023.3330895

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

    Marwa Bastwesy, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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    記述言語:その他  

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

    Toshiki Hayashida, Yugo Nakamura, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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

    Kiichiro Kai, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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  • WatchLogger: Keyboard Typing Words Recognition Based on Smartwatch 査読

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

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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

    Marwa Bastwesy, Hyuckjin Choi, Yutaka Arakawa

    2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)   2023年11月

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

    Bastwesy, MRM; Choi, H; Arakawa, Y

    IEEE ACCESS   11   116384 - 116397   2023年10月   ISSN:2169-3536

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    記述言語:その他   出版者・発行元:IEEE Access  

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

    Bastwesy, MRM; Kai, K; Choi, H; Ishida, S; Arakawa, Y

    2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC   2023-March   2023年3月   ISSN:1525-3511 ISBN:978-1-6654-9122-8

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    記述言語:その他   掲載種別:研究論文(その他学術会議資料等)   出版者・発行元:IEEE Wireless Communications and Networking Conference, WCNC  

    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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  • 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年   ISBN:9784907626525

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    出版者・発行元: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年   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 FOURTEENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK, ICMU   2023年   ISBN:978-4-907626-52-5

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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年   ISBN:9784907626525

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    出版者・発行元: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

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  • 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年   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年   ISBN:9784907626525

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    出版者・発行元: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年   ISBN:978-4-907626-52-5

     詳細を見る

  • 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年   ISBN:9784907626525

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    出版者・発行元: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

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

    Choi H., Fujimoto M., Matsui T., Misaki S., Yasumoto K.

    IEEE Access   10   24395 - 24410   2022年3月   eISSN:2169-3536

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    記述言語:その他   掲載種別:研究論文(学術雑誌)   出版者・発行元:IEEE Access  

    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&#37; of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1&#37; 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&#37; of within-1-person error) than PEM (0.62 MAE, 66.5&#37; of within-1-person error).

    DOI: 10.1109/ACCESS.2022.3155812

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    記述言語:その他   掲載種別:研究論文(その他学術会議資料等)  

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    記述言語:その他   掲載種別:研究論文(その他学術会議資料等)  

    DOI: 10.1109/ICUFN.2017.7993833

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講演・口頭発表等

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    記述言語:その他  

    国名:日本国  

MISC

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    記述言語:その他  

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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年10月

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    記述言語:その他  

  • 椅子の揺れに基づく頷き認識システムの設計と構築

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    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年7月

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    記述言語:その他  

  • 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

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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年5月

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▼全件表示

所属学協会

委員歴

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

    2023年3月 - 2023年8月   

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    団体区分:学協会

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学内運営に関わる各種委員・役職等

  • 2023年3月 - 2023年8月   その他 Technical Program Committee