Classification of EMG Signal for Health Screening Task for Musculoskeletal Disorder


  • Tengku Nor Shuhada Tengku Zawawi
  • Abdul Rahim Abdullah
  • Rubita Sudriman
  • Norhashimah Mohd Saad
  • Jingwei Too
  • Ezreen Farina Shair





Electromyography, Spectrogram, Machine learning classifier, Mean root mean square voltage.


Electromyography signal analysis and classification method for Health Screening Program  for Social Security Organisation (SOCSO) Malaysia is the first time applied using time-frequency distribution (TFD). This paper presents the classification of EMG signals for health screening task for musculoskeletal disorder. A time-frequency method, i.e spectrogram is employed to obtain the data of time and frequency information of the EMG signal. Four machine learning classifier of k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB) and Support Vector Machine (SVM) are implemented to EMG signal. Three out of six tasks (axial rotational task, kneeling reach and kneeling to standing back reach) which focused on the upper limb was performed using Multi Sensor Management ConsensysPRO and functional range on motion (FROM). From the experiment, SVM classifier is outperformed others using the purposed extracted features from spectrogram which is more than 80% except NB with 73.33%. The finding of the study concludes that SVM is suitable to classify EMG signal and can help rehabilitation center to diagnose their patients.




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