Jiang Xianta, Chu Kelvin H T, Khoshnam Mahta, Menon Carlo
MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada.
Sensors (Basel). 2018 Apr 21;18(4):1279. doi: 10.3390/s18041279.
(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.
(1) 背景：步态参数的定量评估可为构建个体步态轮廓、诊断步态异常以及更好地规划恢复正常步态模式的康复方案提供有用信息。在步态分析应用中，客观确定步态周期中的步态阶段是一项关键要求；(2) 方法：在本研究中，探索了使用基于测力肌电图技术的可穿戴步态阶段检测系统的可行性。为此，开发了一个测力肌电图带，并在九名参与者在跑步机上行走时进行了测试。首先对收集到的测力肌电图数据逐样本进行检查，并使用线性判别分析将其分为四个阶段。然后使用一组监督规则从这些分类样本中检测步态阶段事件；(3) 结果：结果表明，测力肌电图带能够正确检测超过99.9%的步态阶段，在12965个步态阶段片段中零插入且仅四个删除。步态阶段检测的平均时间误差为55.2毫秒，相对于相应标记的步幅持续时间而言，这相当于2.1%的误差；(4) 结论：这项概念验证研究证明了测力肌电图技术作为开发可穿戴步态阶段检测系统可行解决方案的可行性。
