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    Measuring human joint movement with IMUs: Implementation in custom-made low cost wireless sensors

    70378.docx (416.8Kb)
    Access Status
    Open access
    Authors
    Xu, W.
    Ortega-Sanchez, C.
    Murray, Iain
    Date
    2018
    Type
    Conference Paper
    Metadata
    Show full item record
    Citation
    Xu, W. and Ortega-Sanchez, C. and Murray, I. 2018. Measuring human joint movement with IMUs: Implementation in custom-made low cost wireless sensors. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD), 13-14 Dec. 2017, Putrajaya, Malaysia.
    Source Conference
    2017 IEEE 15th Student Conference on Research and Development (SCOReD)
    DOI
    10.1109/SCORED.2017.8305399
    ISBN
    9781538621264
    Faculty
    Faculty of Science and Engineering
    Remarks

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/70166
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. This paper presents a inertial measurement unit (IMU) based wireless, wearable sensor system and its algorithm to capture human joint orientation and movement. Many physiotherapy and kinematical studies require a precise analysis of human joint movements. The proposed system provides an economic and flexible solution to measure human joint movement. The system includes several customised low-cost, small, wireless IMU sensors (accelerometer and gyroscope combined) which can be easily attached on any part of the human body. A dongle connected to a computer receives data collected by the sensors in real-Time. Data sets are stored in the computer for later analysis and visualization. The proposed algorithm can accurately extract human joint orientation from the raw measurements of two inertial sensors. Compared to other yaw, pitch and roll orientation algorithms, the presented algorithm only focuses on the relative angle between two sensors instead of using a ground plane reference. The algorithm can be easily embedded into a post data analysis system. With its light data load requirement, the algorithm can also be effectively built onto a real-Time joint orientation capturing system. This paper provides a high-level description of both the hardware platform and the demonstration of the algorithm, and presents step by step plots verifying the algorithm's performance. The system is currently used in a cerebral palsy research study in Australia.

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