基于压力和惯性传感器的步态分析验证研究

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Abstract As intelligent sensors such as acceleration sensor, barometric sensor are used in paramedical environment. Propose a new insole-based GaitAssistant system and a step count algorithm. The algorithm divides the foot area into two parts, the pressure is used to find the initial contact and final left timestamp to obtain the relevant time parameters and the number of steps. The system supports synchronous data collection at the same time, and then we can obtain the step length by double integrate the acceleration data. In order to assess the reliability and accuracy of the system. The subjects were asked to walk on the GAITRite mat while wearing the smart insoles with 3 different speed (fast, slow, normal walk). The results from the GAITRite mat and GaitAssistant system were compared. At the same time, we compared the number of labeled steps with the GaitAssistant results. The results show that the average values of the stand time and step time are the same and the maximum difference average value of swing time is around 0.07s indicating that there is no significant difference between the two systems in terms of major parameter calculations. Step count result also has a high accuracy. This provide a potential possibilities for development of intelligent paramedical system.

Key words： gait analysis step count smart insole wearable sensor healthcare

Received: 03 January 2020

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	An Geng
	Yang Mingjing

Cite this article:

An Geng,Yang Mingjing. Validation study of gait analysis based on pressure and inertial sensor[J]. Electrical Engineering, 2020, 21(6): 45-49.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I6/45

[1] Faisal A, Christina H, Ahmed A, et al.Instrumented wireless smartinsole system for mobile gait analysis: a validation pilot study with tekscan strideway[J]. Journal of Sensor and Actuator Networks, 2018, 7(3): 36.
[2] Wang C, Wang X, Long Z, et al.Estimation of spatial-temporal gait parameters based on the fusion of inertial and film-pressure signals[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2018.
[3] Feng Lin, Aosen Wang, Yan Zhuang, et al.Smart insole: a wearable sensor device for unobtrusive gait monitoring in daily life[J]. IEEE Transactions on Industrial Informatics, 2016, 12(6): 2281-2291.
[4] Anwary A R, Yu Hongnian, Vassallo M.Gait evaluation using procrustes and euclidean distance matrix analysis[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(5): 2021-2029.
[5] Zhao H, Wang Z, Qiu S, et al.IMU-based gait analysis for rehabilitation assessment of patients with gait disorders[C]//2017 4th International Conference on Systems and Informatics (ICSAI), IEEE, 2017.
[6] Yang M, Zheng H, Wang H, et al.IGAIT: An interactive accelerometer based gait analysis system[J]. Comput. Methods Programs Biomed., 2012, 108(2): 715-723.
[7] Jarchi D, Pope J, Lee TKM, et al.A review on accelerometry-based gait analysis and emerging clinical applications[J]. IEEE Reviews in Biomedical Engineering, 2018, 11(99): 177-194.
[8] Fabio A. Storm, Christopher J.Buckley and Claudia Mazzà. Gait event detection in laboratory and real life settings: accuracy of ankle and waist sensor based methods[J]. Gait Posture 2016, 50: 42-46.
[9] Majumder J A, Zerin I, Tamma C P, et al.A wireless smart-shoe system for gait assistance[C]//Biomedical Conference (GLBC) 2015 IEEE Great Lakes, 2015: 1-4.
[10] Kuys S S, Brauer S G, Ada L.Test-retest reliability of the GAITRite system in people with stroke undergoing rehabilitation[J]. Disability and Rehabilitation, 2011, 33(19/20): 1848-1853.
[11] McCalmont. G., Morrow. P., Zheng H., et al. eZi-Gait: Toward an AI gait analysis and assistant system[C]// 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018: 2280-2286.
[12] Nhan Duc Nguyen, Duong Trong Bui, Phuc Huu Truong, et al.Classification of five ambulatory activities regarding stair and incline walking using smart shoes[J]. IEEE Sensors Journal, 2018, 18(13): 5422-5428.
[13] Simona Crea, Marco Donati, Stefano Marco Maria De Rossi, et al. A wireless flexible sensorized insole for gait analysis[J]. Sensors 2014, 14: 1073-1093.
[14] Ferrari A, Ginis P, Hardegger M, et al.A mobile kalman-filter based solution for the real-time estima-tion of spatio-temporal gait parameters[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering: a Publication of the IEEE Engineering in Medicine and Biology Society, 2016, 24(7): 764-773.
[15] Salarian A, Burkhard P R, Vingerhoets F J, et al.A novel approach to reducing number of sensing units for wearable gait analysis systems[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(1): 72-77.
[16] Mariani B, Hoskovec C, Rochat S, et al.3D gait assessment in young and elderly subjects using foot-worn inertial sensors[J]. Journal of Biomechanics, 2010, 43(15): 2999-3006.