Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain.
Department of Systems Engineering, National Autonomous University of Honduras, Tegucigalpa 11101, Honduras.
Biosensors (Basel). 2023 Jun 30;13(7):697. doi: 10.3390/bios13070697.
In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.
在这项工作中，我们评估了从运动感应相机获得的人类可操作性指数与从上肢表面肌电图 (sEMG) 信号中提取的各种肌肉因素之间的关系，这些运动包括肩部、肘部和腕关节。结果表明上肢运动与可操作性之间存在特定的联系，揭示了极端姿势的可操作性较低，即手臂完全伸展或完全弯曲时。然而，sEMG 信号的平均活动与可操作性因素之间没有明显的相关性，这表明肌肉活动至少只是间接地与人体姿势奇点相关。如果存在任何推断这些相关性的方法，可能是使用先进的深度学习技术。我们还分析了一组肌电图指标，这些指标深入了解了在运动过程中肌肉用力的分布情况。这组指标可用于根据康复治疗的里程碑获得运动序列的定量评估的良好指标，或者在工作任务中规划更符合人体工程学和更能承受的运动阶段。
