School of Engineering, Monash University, Selangor, Malaysia.
Sunway University Business School, Sunway University, Selangor, Malaysia.
Technol Health Care. 2020;28(6):675-684. doi: 10.3233/THC-192034.
Walking is one of the important actions of the human body. For this purpose, the human brain communicates with leg muscles through the nervous system. Based on the walking path, leg muscles act differently. Therefore, there should be a relation between the activity of leg muscles and the path of movement.
In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.
Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.
Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.
The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
行走是人体的重要动作之一。为此,人脑通过神经系统与腿部肌肉进行交流。根据行走路径,腿部肌肉的作用方式不同。因此,腿部肌肉的活动与运动路径之间应该存在一定的关系。
为了解决这个问题,我们分析了腿部肌肉活动与运动路径变化之间的关系。
由于肌电图(EMG)信号是肌肉活动的特征,而运动路径具有复杂的结构,我们使用熵分析来连接它们的结构。计算 EMG 信号和行走路径的香农熵,以关联它们的信息量。
基于获得的结果,在信息含量更大的路径上行走会导致 EMG 信号中的信息含量更大,这一结果得到了统计分析结果的支持。这使我们能够分析肌肉活动与行走路径之间的关系。
本研究中采用的分析方法可用于研究大脑或心脏反应与行走路径之间的关系。