College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
School of Design and Art, Shanghai Dianji University, Shanghai 200240, China.
J Healthc Eng. 2021 Mar 24;2021:8879061. doi: 10.1155/2021/8879061. eCollection 2021.
The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.
人体运动的动态检测十分重要，广泛应用于运动状态捕捉和康复工程领域。本研究基于上肢表面肌电（sEMG）信号、三轴加速度和下肢足底压力信号的多模态信息，提出了有效的虚拟驾驶控制和步态识别方法，构建了一种有效的可穿戴人体姿态检测方法。首先，使用移动平均窗口和阈值比较对上肢的 sEMG 信号进行分割，提取小波系数的标准差和奇异值作为特征。经过优化支持向量机（SVM）算法的训练和分类，实现了三种虚拟驾驶动作的实时检测和分析，平均识别准确率为 90.90%。其次，提取三轴加速度的均值、标准差、方差和小波能量谱，并将这些参数与足底压力结合作为步态特征。选用优化的 SVM 进行步态识别，平均准确率为 90.48%。实验结果表明，通过在上下肢穿戴不同的传感器组合，可以动态检测运动姿态信息，可用于虚拟康复系统和行走辅助系统的设计。
