Design of Gait Detection System Based on FCM Algorithm
Abstract
high accuracy and portable detection equipment and real-time detection algorithm. This paper introduces a real-time gait detection
system, this kind of gait detection system using machine learning algorithm based on FCM clustering analysis and calibration of
the sensor data filter algorithm based on rules, the kinematic data is divided into five different gait events, namely the heel strike ,
foot flat, heel off , toe off and initial swing phase . Compared with 3D posture capture experiment equipment, the accuracy of gait
event detection of the proposed gait detection system is verified to be highly reliable.
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[1] Lin Huiqing, He Jianmin, Chen Shumin, Chen Xianghua, Liang Wanchan, Zhong Lixia, Chen Xiaozheng. Nursing application of plantar pressure measurement in the prevention of high-risk groups of diabetic foot [J]. Chinese Community Physician, 2020, 36(18): 151-152.
[2] S. Borel, P. Schneider, and C. Newman, “Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy,” Gait & Posture, vol. 33, no. 4, pp.
727 –729, 2011. View at: Publisher Site | Google Scholar
[3] W. Dickens and M. Smith, “Validation of a visual gait assessment scale for children with hemiplegic cerebral palsy,” Gait & Posture, vol. 23, no. 1, pp. 78–82, 2006. View at: Publisher Site | Google
Scholar
[4] S. Liu, Q. Wang, and Y. Luo, “A review of applications of visual inspection technology based on image Processing in the railway industry,” Transportation Safety and Environment, vol. 1, no. 3, pp. 185–204, 2020. View at: Google Scholar
[5] Liu Xiyang, Feng Siling, Chen Xianmei, Liu Jinrong. Recognition of Plantar Pressure Distribution Pattern Based on Fuzzy Theory [J]. Journal of Natural Science of Xiangtan University, 2016, 38(01): 93-96.
[6] Qu Binjie, Sun Shaoyuan, Samah A.F. Manssor, Zhao Guoshun. Multi-view gait recognition method based on perspective conversion [J]. Computer Engineering, 2021, 47(06): 210-216.7
[7] Young, AJ; Simon, AM; Hargrove, LJ A training method for locomotion mode prediction using powered lower limb prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 671–677.
[8] Liu Z, Lin W, Geng Y, et al. Intent pattern recognition of lowerlimb motion based on mechanical sensors[J]. IEEE/CAA Journal of AutomaticaSinica, 2017, 4(4): 651-660.
DOI: http://dx.doi.org/10.18686/ahe.v6i15.5143
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