Park Heesu, Han Sungmin, Sung Joohwan, Hwang Soree, Youn Inchan, Kim Seung-Jong
Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea.
Department of Biomedical Engineering, Korea University College of Medicine, Seoul, Republic of Korea.
Front Hum Neurosci. 2023 May 17;17:1201935. doi: 10.3389/fnhum.2023.1201935. eCollection 2023.
The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.
准确检测步态阶段对于监测和诊断神经及肌肉骨骼疾病以及精确控制下肢辅助设备至关重要。在研究神经疾病的运动模式识别和康复过程中，模块化组织的概念已被证明是有用的，该概念涉及肌肉群的共同激活以产生各种运动行为。本研究旨在探讨与传统特征（如时域和小波特征）相比，肌肉协同特征是否能为步态事件提供更准确、更可靠的分类。为此，八名健康个体参与了本研究，并将无线肌电图传感器附着在每个下肢的四块肌肉上，以测量行走过程中的肌电图（EMG）信号。EMG信号被分割并标记为两类（站立和摆动）和三类（承重、单肢支撑和肢体推进）步态阶段。使用非负矩阵分解（NNMF）来识别对步态有贡献的特定肌肉群，并对运动系统的功能组织进行分析。使用四种不同的机器学习算法对步态阶段进行分类：决策树（DT）、k近邻（KNN）、支持向量机（SVM）和神经网络（NN）。结果表明，肌肉协同特征比其他EMG特征具有更好的分类准确率。这一发现支持了肌肉协同能够实现准确步态阶段分类的假设。总体而言，该研究提出了一种新颖的步态分析方法，并突出了肌肉协同作为步态阶段检测工具的潜力。
