文献类型：期刊文献

英文题名：STUDY OF GAIT PATTERN RECOGNITION BASED ON FUSION OF MECHANOMYOGRAPHY AND ATTITUDE ANGLE SIGNAL

作者：Yu, Jing[1];Zhang, Yue[1];Xia, Chunming[1]

机构：[1]East China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：20

期号：2

外文期刊名：JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY

收录：;EI(收录号：20201308343417);WOS:【SCI-EXPANDED(收录号:WOS:000524003800007)】；

语种：英文

外文关键词：Gait recognition; rehabilitation; mechanomyography (MMG); attitude angle; hidden Markov model (HMM)

摘要：The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%.