Zhao Dongwei, Ye Xiangming, Wang Song, Zhang Chenfeng, Sun Shouqian, Zhang Xuequn, Cheng Ruidong
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Center for Rehabilitation Medicine, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Zhejiang Engineering Research Center for Digital-Intelligent Rehabilitation Equipment, Hangzhou, China.
Sci Rep. 2025 May 30;15(1):18969. doi: 10.1038/s41598-025-02864-5.
This study aims to classify five typical motion states of the human upper limb based on surface electromyography signals, thereby supporting the real-time control system of an assistive upper limb exoskeleton. We propose a deep learning model combining convolutional neural networks, bidirectional long short-term memory networks, and attention mechanism to enhance the accuracy of motion state recognition in complex scenarios. Surface electromyography data were collected from ten participants for the biceps, triceps, and deltoid muscles, covering five representative states: resting, mild activity, rapid movement, dynamic load-bearing, and static load-bearing. Following the systematic fusion of multi-domain features spanning time, morphological, frequency, and cepstral characteristics, temporal features were structured through sliding window segmentation to serve as inputs for the proposed model. The proposed model achieved a classification accuracy of 97.29% on the test set, with an average accuracy of 88.17 ± 5.39% under leave-one-subject-out cross-validation, outperforming baseline algorithms. These findings highlight the model's potential in motion state classification, facilitating advanced, intelligent control capabilities of human-exoskeleton systems.
本研究旨在基于表面肌电信号对人类上肢的五种典型运动状态进行分类，从而为辅助上肢外骨骼的实时控制系统提供支持。我们提出一种结合卷积神经网络、双向长短期记忆网络和注意力机制的深度学习模型，以提高复杂场景下运动状态识别的准确性。从十名参与者的肱二头肌、肱三头肌和三角肌收集表面肌电数据，涵盖五种代表性状态：休息、轻度活动、快速运动、动态负重和静态负重。在对跨越时间、形态、频率和倒谱特征的多域特征进行系统融合之后，通过滑动窗口分割构建时间特征，作为所提模型的输入。所提模型在测试集上的分类准确率达到97.29%，在留一法交叉验证下平均准确率为88.17±5.39%，优于基线算法。这些发现凸显了该模型在运动状态分类中的潜力，有助于实现人机外骨骼系统先进的智能控制能力。
