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Demonstrating hitonavi-μ: a novel wearable LiDAR for human activity recognition

Published: 14 October 2022 Publication History

Abstract

In this paper, we demonstrate a brand new design of a wearable device that enables privacy-preserving human activity recognition based on light-weight compact-size LiDAR. The device scans and represents the surrounding environment in 3D point clouds form. The system further processes this representation to define discriminative features that facilitate recognizing human activity on edge. These features are extracted using Spatio-temporal probabilistic clustering and fisher vector representations and then used to train a classification model for activity recognition purposes. Implementation and evaluation of the proposed system confirm its efficient ability to identify human activities.

References

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L Minh Dang, Kyungbok Min, Hanxiang Wang, Md Jalil Piran, Cheol Hee Lee, and Hyeonjoon Moon. 2020. Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition 108 (2020), 107561.
[2]
Hikaru Katayama, Teruhiro Mizomoto, Hamada Rizk, and Hirozumi Yamaguchi. 2022. You Work We Care: Sitting Posture Assessment Based on Point Cloud Data. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). 121--123.
[3]
Yuma Okochi, Hamada Rizk, Tatuya Amano, and Hirozumi Yamaguchi. 2022. Object Recognition from 3D Point Cloud on Resource-Constrained Edge Device. In The 18th International Conference on Wireless and Mobile Computing, Networking and Communications.
[4]
Yuma Okochi, Hamada Rizk, and Hirozumi Yamaguchi. 2022. On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR. In 2022 18th International Conference on Intelligent Environments (IE). 1--4.
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Hamada Rizk, Hirozumi Yamaguchi, Moustafa Youssef, and Teruo Higashino. 2020. Gain Without Pain: Enabling Fingerprinting-Based Indoor Localization Using Tracking Scanners. In SigSpatial. 550--559.
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Hamada Rizk, Hirozumi Yamaguchi, Moustafa Youssef, and Teruo Higashino. 2022. Laser Range Scanners for Enabling Zero-Overhead WiFi-Based Indoor Localization System. ACM Trans. Spatial Algorithms Syst. (2022).
[7]
Jorge Sánchez, Florent Perronnin, Thomas Mensink, and Jakob Verbeek. 2013. Image classification with the fisher vector: Theory and practice. International journal of computer vision 105, 3 (2013), 222--245.
[8]
Shota Yamada, Hamada Rizk, and Hirozumi Yamaguchi. 2022. An Accurate Point Cloud-Based Human Identification Using Micro-Size LiDAR. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). 569--574.

Cited By

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  • (2024)RISense: 6G-Enhanced Human Activity Recognition System with RIS and Deep LDA2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00035(119-128)Online publication date: 24-Jun-2024
  • (2024)Low-Cost LIDAR-Based Monitoring System for Fall DetectionIEEE Access10.1109/ACCESS.2024.340165112(72051-72061)Online publication date: 2024
  • (2024)Fall Detection and Assessment Using Multitask Learning and Micro-sized LiDAR in Elderly CareMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63992-0_19(280-293)Online publication date: 19-Jul-2024

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  1. Demonstrating hitonavi-μ: a novel wearable LiDAR for human activity recognition

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    cover image ACM Conferences
    MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
    October 2022
    932 pages
    ISBN:9781450391818
    DOI:10.1145/3495243
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 14 October 2022

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    Author Tags

    1. LiDAR
    2. human activity recognition
    3. point cloud processing
    4. response to COVID-19

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    • Demonstration

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    • JST, Japan

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    ACM MobiCom '22
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    Overall Acceptance Rate 440 of 2,972 submissions, 15%

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    • (2024)RISense: 6G-Enhanced Human Activity Recognition System with RIS and Deep LDA2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00035(119-128)Online publication date: 24-Jun-2024
    • (2024)Low-Cost LIDAR-Based Monitoring System for Fall DetectionIEEE Access10.1109/ACCESS.2024.340165112(72051-72061)Online publication date: 2024
    • (2024)Fall Detection and Assessment Using Multitask Learning and Micro-sized LiDAR in Elderly CareMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63992-0_19(280-293)Online publication date: 19-Jul-2024

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