Performance Assessment of the Optimum Feature Extraction for Upper-limb Stroke Rehabilitation using Angular Separation Method

Authors

  • Mohd Saiful Hazam Majid Advance Computing and Sustainable Research Group (AICOS), UniMAP. School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Bahagian Sumber Manusia, Majlis Amanah Rakyat (MARA)
  • Wan Khairunizam Advance Computing and Sustainable Research Group (AICOS), UniMAP, School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Hashimah Ali School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • I. Zunaidi School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Shahriman AB School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Zuradzman MR School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Hazry D School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Mohd Asri Ariffin School of Health Sciences Kampus Kesihatan Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.

Keywords:

Angular Separation Method, Electromyogram (EMG), Feature Extraction, Rehabilitation, Upper-limb,

Abstract

Most of the human everyday activities will require the use of their upper-limb muscles. The pattern of upper-limb muscle movement can be used to estimate upper-limb motions. Fundamental arm movement which is part of upper-limb muscle rehabilitation activity has been studied in order to investigate the time domain features, frequency domain, and time-frequency domain from the surface electromyogram (sEMG) signal of the upper-limb muscle. The relationship of electromyogram (EMG) signal and the rehabilitation exercise of related upper limb muscles movements are analyzed in this study. Then the features from the three domains were compared using Angular Separation Method to determine optimal feature. The result shows that MinWT has the best value of similarity which is 0.98, followed by a MeanWT feature which resulted in 0.91 of similarity. These results of EMG signal feature extraction can be used later in the study of human upper-limb muscle especially for analyzing EMG signal from patient undergone a rehabilitation treatment.

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Published

2018-05-29

How to Cite

Majid, M. S. H., Khairunizam, W., Ali, H., Zunaidi, I., AB, S., MR, Z., D, H., & Ariffin, M. A. (2018). Performance Assessment of the Optimum Feature Extraction for Upper-limb Stroke Rehabilitation using Angular Separation Method. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 99–103. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4131

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