基于惯性传感器的穿戴式步态分析系统设计与实现

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摘要步态分析是对下肢运动情况量化评估的重要手段。本文采用基于惯性传感器的步态时空参数估计算法,根据穿戴在小腿上的惯性传感器节点检测步态事件点,再对穿戴在脚跟处的惯性传感器加速度信号进行双重积分计算步长,根据步态时相校正加速度信号以提高步长估计精度。本文设计并实现了基于惯性传感器的步态数据节点采集,完成了硬件和软件的设计。为了验证系统的可靠性和准确性,对3个志愿者进行实验。比较本文设计的步态分析系统和商用locometrix步态分析仪的结果,步长、步速和步频的平均相对误差分别为5.21%、4.51%和11.87%。结果表明利用本文研制的穿戴式步态分析系统能精确估计步态时空参数。

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	李江慧
	连春快
	李玉榕

关键词 ：步态分析, 惯性传感器, 步态时空参数, 智慧医疗

Abstract：Gait analysis is an important method for the quantitative evaluation of lower limb movement. This paper uses the gait space-time parameter estimation algorithm based on inertial sensor. Firstly, the gait event point is detected according to the inertial sensor worn on the calf. Then the step length is calculated by the double integration of the acceleration signal of the inertial sensor worn on the heel, and the acceleration signal is calibrated according to the gait phase to improve the estimation accuracy of step length. The gait acquisition node based on inertial signals is designed and implemented in this paper. The hardware and software design are completed. To verify the reliability and accuracy of the system, three volunteers are recruited for the experiment. Compared with the results of the locometrix device, which is a kind of commercial gait analyzer, the average relative error of step length, speed and step frequency are 5.21%, 4.51% and 11.87% respectively. The results show that the developed gait analysis system can accurately estimate the gait space-time parameters.

Key words： gait analysis inertial sensor gait space-time parameters smart medical care

收稿日期: 2021-01-25

基金资助:国家自然科学基金（61773124）;福建省自然科学基金（2019J01544）

作者简介: 李江慧（2000—）,女,本科,主要研究方向为智能康复设备研发。

引用本文:

李江慧, 连春快, 李玉榕. 基于惯性传感器的穿戴式步态分析系统设计与实现[J]. 电气技术, 2021, 22(9): 14-21. LI Jianghui, LIAN Chunkuai, LI Yurong. Design and implementation of wearable gait analysis system based on inertial sensor. Electrical Engineering, 2021, 22(9): 14-21.

链接本文:

http://dqjs.cesmedia.cn/CN/Y2021/V22/I9/14

[1] 钱竞光, 宋雅伟, 叶强, 等. 步行动作的生物力学原理及其步态分析[J]. 南京体育学院学报(自然科学版), 2006(4): 1-7, 39.
[2] NONNEKES J, GOSELINK R J M, RUZICKA E, et al. Neurological disorders of gait, balance and posture: a sign-based approach (review)[J]. Nature Reviews Neurology, 2018, 14(3): 183-189.
[3] ZAGO M, SFORZA C, BONARDI D R, et al.Gait analysis in patients with chronic obstructive pulmonary disease: a systematic review[J]. Gait & Posture, 2018, 61: 408-415.
[4] KIRSCHBERG J, GORALSKI S, LAYHER F, et al.Normalized gait analysis parameters are closely related to patient-reported outcome measures after total knee arthroplasty[J]. Archives of Orthopaedic and Trauma Surgery, 2018, 138(5): 711-717.
[5] KIM H, MILLER L M, FEDULOW I, et al.Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 21(2): 153-164.
[6] DONISI L, PAGANO G, CESARELLI G, et al.Bench marking between two wearable inertial systems for gait analysis based on a different sensor placement using several statistical approaches[J]. Measurement, 2021, 173: 108642.
[7] CHO Y S, JANG S H, CHO J S, et al.Evaluation of validity and reliability of inertial measurement unit- based gait analysis systems[J]. Annals of Rehabi- litation Medicine, 2018, 42(6): 872-883.
[8] ZHENG H, BLACK N D, HARRIS N D.Position- sensing technologies for movement analysis in stroke rehabilitation[J]. Medical & Biological Engineering & Computing, 2005, 43(4): 413-420.
[9] 荷兰Xsens Technologies公司技术网站. XSENS Products技术资料[EB/OL]. [201912]. http://www.xsens.com.
[10] BOUDARHAM J, ROCHE N, PRADON D, et al.Variations in kinematics during clinical gait analysis in stroke patients[J]. PloS one, 2013, 8(6): e66421.
[11] KARASAWA Y, TERUYAMA Y, WATANABE T.A trial of making reference gait data for simple gait evaluation system with wireless inertial sensors[C]// 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013: 3427-3430.
[12] WU Yinfeng, SU Yiwen, FENG Renjian, et al.Wearable- sensor-based pre-impact fall detection system with a hierarchical classifier[J]. Measurement, 2019(140): 283-292.
[13] BAMBERG S J M, BENBASAT A Y, SCARBOROUGH D M, et al. Gait analysis using a shoe-integrated wire- less sensor system[J]. IEEE Transactions on Infor- mation Technology in Biomedicine, 2008, 12(4): 413-423.
[14] 卢军. 助力外骨骼机器人随动控制算法设计与实现[D]. 成都: 电子科技大学, 2016.
[15] 欧阳高询. 双目结构光深度获取研究及光学平台构建[D]. 西安: 西安电子科技大学, 2015.
[16] 王涵. 无人机航迹规划及导航定位系统研究[D]. 杭州: 浙江大学, 2017.
[17] 李洪凤, 林康, 李斌. 基于四元数的永磁动量球位置/电流双闭环控制[J]. 电工技术学报, 2019, 34(2): 484-492.
[18] 安耕, 杨明静. 基于压力和惯性传感器的步态分析验证研究[J]. 电气技术, 2020, 21(6): 45-49.