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IMU-Kinect: a motion sensor-based gait monitoring system for intelligent healthcare

Published: 09 September 2019 Publication History

Abstract

Gait rehabilitation is a common method of postoperative recovery after the user sustains an injury or disability. However, traditional gait rehabilitations are usually performed under the supervision of rehabilitation specialists, meaning the patients can not receive adequate care continuously. In this paper, we propose IMU-Kinect, a novel system to remotely and continuously monitor the gait rehabilitation via the wearable kit. This system consists of a wearable hardware platform and a user-friendly software application. The hardware platform is composed of four Inertial Measurement Units (IMU), which are attached on the shanks and thighs of the human body. The software application is able to estimate the rotation and displacement of these sensors, then reconstruct the gait movements and calculate the gait parameters according to the geometric model of human lower limbs. Based on IMU-Kinect system, the users of gait rehabilitation just need to walk normally by wearing the IMU-Kinect kit, and then the rehabilitation specialists can analyze the status of postoperative recovery by remotely viewing the animations about users' gait movements and charts of the general gait parameters. Extend experiments in real environment show that our system can efficiently track the gait movements with 9% rotation and displacement error.

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References

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Alvaro Muro-De-La-Herran, Begonya Garcia-Zapirain, and Amaia Mendez-Zorrilla. 2014. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14, 2 (2014), 3362--3394.
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  1. IMU-Kinect: a motion sensor-based gait monitoring system for intelligent healthcare

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    cover image ACM Conferences
    UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
    September 2019
    1234 pages
    ISBN:9781450368698
    DOI:10.1145/3341162
    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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    Publication History

    Published: 09 September 2019

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    Author Tags

    1. gait monitoring
    2. intelligent healthcare
    3. wearable computing

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    • (2024)Learn From Others and Be Yourself in Federated Human Activity Recognition via Attention-Based Pairwise CollaborationsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.335126073(1-15)Online publication date: 2024
    • (2024)A Self-Supervised Human Activity Recognition Approach via Body Sensor Networks in Smart CityIEEE Sensors Journal10.1109/JSEN.2023.328260124:5(5476-5485)Online publication date: 1-Mar-2024
    • (2023)IoT-Enabled Gait Assessment: The Next Step for Habitual MonitoringSensors10.3390/s2308410023:8(4100)Online publication date: 19-Apr-2023
    • (2023)Auto-GaitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808457:1(1-19)Online publication date: 28-Mar-2023
    • (2023)Channel-Equalization-HAR: A Light-weight Convolutional Neural Network for Wearable Sensor Based Human Activity RecognitionIEEE Transactions on Mobile Computing10.1109/TMC.2022.317481622:9(5064-5077)Online publication date: 1-Sep-2023
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    • (2023)Contrastive Learning Based Human Activity Recognition Using Body Sensors2023 9th International Conference on Big Data Computing and Communications (BigCom)10.1109/BIGCOM61073.2023.00043(264-270)Online publication date: 4-Aug-2023
    • (2022)Monitoring of motor function in the rehabilitation roomProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3534761(683-688)Online publication date: 29-Jun-2022
    • (2022)Wearable Gait Recognition Systems Based on MEMS Pressure and Inertial Sensors: A ReviewIEEE Sensors Journal10.1109/JSEN.2021.313158222:2(1092-1104)Online publication date: 15-Jan-2022
    • (2022)InertiEAR: Automatic and Device-independent IMU-based Eavesdropping on SmartphonesIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796890(1129-1138)Online publication date: 2-May-2022
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