A wearable comprehensive data sampling system for gait analysis

被引:5
|
作者
Fang Z. [1 ]
Yang Z. [1 ]
Wang Q. [1 ]
Wang C. [1 ]
Chen S. [1 ]
机构
[1] School of Aerospace Engineering, Xiamen University, Xiamen
来源
Chen, Siyuan (chensiyuan@xmu.edu.cn) | 2018年 / Taylor and Francis Ltd.卷 / 42期
基金
中国国家自然科学基金;
关键词
embedded system; Gait analysis; rehabilitation; signal sampling; wearable device;
D O I
10.1080/03091902.2018.1430184
中图分类号
学科分类号
摘要
Gait analysis is important for lower limb movement evaluation and rehabilitation research. More and more laboratories focus on it. Researchers need biomechanical data sampling equipment to obtain original signals for their analysis, sometimes even need kinds of signals for data fusion processing. But, the market supply of relative products is very limited. Moreover, one device acquires only one kind of signal, and needs computer as the control centre. So, there are two problems: moving range limitation, and synchronisation in data fusion processing. Most researchers plan experiments only indoors, and sometimes need to do secondary development for data fusion synchronisation. This article represents a compact-embedded system for lower limb biomechanical signals acquisition. Four kinds of signals are collected: foot plantar pressure, inertial measurement, laser distance sensing and electromyography. The embedded circuit is powered by a lithium battery. All the signals are synchronised by the embedded clock, and stored in secure digital memory card for offline analysis. It is convenient to plan experiments in all kinds of terrains indoors or outdoors. It is unique for its wearable, low power and comprehensive characters. Experimental results show that it is a useful tool for gait analysing research. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:335 / 343
页数:8
相关论文
共 50 条
  • [21] Gait Analysis Using Wearable Sensors
    Bhosale, Tanmav
    Kudale, Hemant
    Kumthekar, Varun
    Garude, Shreyash
    Dhumal, Prasad
    2015 INTERNATIONAL CONFERENCE ON ENERGY SYSTEMS AND APPLICATIONS, 2015, : 267 - 269
  • [22] Gait Recognition Based on Tensor Analysis of Acceleration Data from Wearable Sensors
    Bichave, Kshitij
    Brewer, Owen
    Gusinov, Max
    Markopoulos, Panos P.
    Puchades, Ivan
    2018 IEEE WESTERN NEW YORK IMAGE AND SIGNAL PROCESSING WORKSHOP (WNYISPW), 2018,
  • [23] TOWARD A MORE COMPREHENSIVE GAIT DISORDER ANALYSIS EXPERT SYSTEM
    BEKEY, GA
    KIM, JWJ
    1989 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-3: CONFERENCE PROCEEDINGS, 1989, : 523 - 525
  • [24] A Wearable Pedestrian Localization and Gait Identification System Using Kalman Filtered Inertial Data
    Hajati, Nasim
    Rezaeizadeh, Amin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [25] A wearable acceleration sensor system for gait recognition
    Rong, Liu
    Jianzhong, Zhou
    Ming, Liu
    Xiangfeng, Hou
    ICIEA 2007: 2ND IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-4, PROCEEDINGS, 2007, : 2654 - +
  • [26] Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis
    Kentaro Homan
    Keizo Yamamoto
    Ken Kadoya
    Naoki Ishida
    Norimasa Iwasaki
    BMC Sports Science, Medicine and Rehabilitation, 14
  • [27] Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis
    Homan, Kentaro
    Yamamoto, Keizo
    Kadoya, Ken
    Ishida, Naoki
    Iwasaki, Norimasa
    BMC SPORTS SCIENCE MEDICINE AND REHABILITATION, 2022, 14 (01)
  • [28] Review wearable sensing system for gait recognition
    Gelan Yang
    Wei Tan
    Huixia Jin
    Tuo Zhao
    Li Tu
    Cluster Computing, 2019, 22 : 3021 - 3029
  • [29] A WEARABLE GAIT MONITOR AND TERRAIN PREDICTION SYSTEM
    Sullivan, Christopher
    DeBartolo, Elizabeth
    Lamkin-Kennard, Kathleen
    PROCEEDINGS OF THE ASME SUMMER BIOENGINEERING CONFERENCE - 2013, PT B, 2014,
  • [30] GaitWEAR: An Augmented Wearable System for Gait Quantification
    Raghuvanshi, Ankita
    Mitra, Paromita
    Rane, Dharma
    Kumar, Suhagiya Dharmik
    Lahiri, Uttama
    IEEE SENSORS JOURNAL, 2024, 24 (21) : 35673 - 35685