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

Will you stop doing assessments and do work that results in effective stroke rehab?  These wastes of time are all because we have NO stroke strategy and NO stroke leadership.
Performance Assessment of the Optimum Feature Extraction for Upper-limb Stroke Rehabilitation using Angular Separation Method
Mohd Saiful Hazam Majid1,2,3, Wan Khairunizam1,2, Hashimah Ali2, I. Zunaidi2, Shahriman AB2, Zuradzman MR2, Hazry D2 and Mohd Asri Ariffin4 1Advance Computing and Sustainable Research Group (AICOS), UniMAP, 2School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. 3Bahagian Sumber Manusia, Majlis Amanah Rakyat (MARA) 4School of Health Sciences Kampus Kesihatan Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia. khairunizam@unimap.edu.my


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.