title: Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.
abstract: Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.
authors.affiliation: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China. hui.zhou@siat.ac.cn.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhou
authors.affiliation: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China. rincyjining@163.com.
authors.forename: Ning
authors.initials: N
authors.lastname: Ji
authors.affiliation: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China. samuel@siat.ac.cn.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China. samuel@siat.ac.cn.
authors.forename: Oluwarotimi Williams
authors.initials: OW
authors.lastname: Samuel
authors.affiliation: Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China. caoyafei64@163.com.
authors.forename: Yafei
authors.initials: Y
authors.lastname: Cao
authors.affiliation: School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China. charlottezhao@foxmail.com.
authors.forename: Zheyi
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China. sx.chen@siat.ac.cn.
authors.forename: Shixiong
authors.initials: S
authors.lastname: Chen
authors.affiliation: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China. gl.li@siat.ac.cn.
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D005684:Gait / Q000502:physiology; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology; D058067:Wavelet Analysis
keywords: accelerometer; continuous wavelet transform; gait event detection; heuristics; stairs walking