Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan.
Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan.
Sensors (Basel). 2024 Jan 23;24(3):734. doi: 10.3390/s24030734.
Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.
步态分析在过去几十年中一直被研究，是评估旨在改善步态的手术技术效果的最佳方法。治疗医生可以了解步态问题的类型，深入了解病因，并通过步态分析找到最佳的治疗方法。步态参数包括运动学的运动学，包括时间和空间参数，并且缺乏骨骼肌肉的活动信息。因此，步态分析不仅测量运动学的三维时空图，还测量下肢的表面肌电图（sEMG）。现在，可穿戴的 GaitUp Physilog 惯性传感器可以轻松测量受试者在普通地面上行走时的步态参数。但是，它无法测量肌肉活动。本研究的目的是使用下肢的 sEMG 测量步态参数。使用自制的无线设备测量来自左右脚的股外侧肌和小腿三头肌的 sEMG。本研究招募了 20 名骨骼肌指数（SMI）低于 5.7kg/m2 的年轻女性受试者，并使用 InBody 270 仪器进行检查。使用 GaitUp Physilog 可穿戴惯性传感器和三种机器学习模型（随机森林（RF）、决策树（DT）和 XGBoost）来测量 sEMG 的四个参数来估计 23 个步态参数。结果表明，可以很好地估计 14 个步态参数，其相关系数高于 0.800。本研究标志着仅使用 sEMG 对步态进行更全面分析的迈出了一步。
