Affiliations: [a] School of Engineering, Monash University, Selangor, Malaysia | [b] Sunway University Business School, Sunway University, Selangor, Malaysia | [c] School of Pharmacy, Monash University, Selangor, Malaysia | [d] Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, Australia
Correspondence:
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Corresponding author: Hamidreza Namazi, School of Engineering, Monash University, Selangor, Malaysia. E-mail: [email protected].
Abstract: BACKGROUND: 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. OBJECTIVE: In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement. METHOD: 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. RESULTS: 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. CONCLUSION: The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
Keywords: Leg muscle, walking path, electromyography (EMG) signal, Shannon entropy, information
