Jiang Yongyu, Chen Christine, Zhang Xiaodong, Chen Chaoyang, Zhou Yang, Ni Guoxin, Muh Stephanie, Lemos Stephen
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
Department of Computer Science, College of Engineering, University of Michigan, Ann Arbor, USA.
Comput Methods Programs Biomed. 2020 Dec;197:105721. doi: 10.1016/j.cmpb.2020.105721. Epub 2020 Aug 25.
Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy.
A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained.
Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models.
The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy.
表面肌电图（sEMG）已用于机器人康复工程中对手部假肢或肘部外骨骼的自主控制，然而，将sEMG用于上肢外骨骼的自主控制尚未得到完善发展。我们研究的长期目标是处理肩部肌肉生物电信号，用于康复机器人辅助设备的运动控制。本研究的目的包括：1）测试机器学习算法在利用肩部和上肢肌肉的sEMG信号进行肩部运动模式识别中的可行性，2）研究运动速度、个体差异、肌电图记录设备以及肌电图数据集数量对肩部运动模式识别准确性的影响。
构建了一种新型卷积神经网络（CNN）结构，用于处理来自12块肌肉的肌电信号，以识别上臂运动的模式，包括静止、饮水、前后运动和外展运动。使用方差分析、广义线性模型单变量分析和卡方检验，对不同运动速度、个体之间以及肌电图记录设备下CNN模型进行模式识别的准确性进行统计分析。通过逐渐增加用于CNN模型训练的肌电图数据集数量，直至获得最高准确性，研究其对识别准确性的影响。
结果表明，正常速度CNN模型在运动模式识别中的准确性，正常速度运动为97.57%，快速速度运动为97.07%。跨个体CNN模型在运动模式识别中的准确性为79.64%。跨设备CNN模型在运动模式识别中的准确性，正常速度运动为88.93%，混合速度为80.87%。不同CNN模型之间的模式识别准确性存在统计学差异。
上肢运动中肩部和上臂肌肉的肌电信号可通过CNN算法进行处理，以识别上肢的相同运动，包括饮水、前/后、外展和静止。由指定运动速度的肌电图数据集训练的简单CNN模型能够准确检测相同运动速度的运动模式，与用于各种运动速度模式识别的其他混合CNN模型相比，准确率最高。增加用于CNN模型训练的肌电图数据集数量可提高模式识别准确性。
