• Nida Sae Jong Faculty of Engineering, Princess of Naradhiwas University, Narathiwat, 96000, Thailand
  • Sakariya Sa-e Faculty of Engineering, Princess of Naradhiwas University, Narathiwat, 96000, Thailand
  • Ahmad Tirasor Faculty of Engineering, Princess of Naradhiwas University, Narathiwat, 96000, Thailand
  • Nifadila Mama Faculty of Engineering, Princess of Naradhiwas University, Narathiwat, 96000, Thailand
  • Hassan Dao Faculty of Engineering, Princess of Naradhiwas University, Narathiwat, 96000, Thailand




upper limb, hand prosthesis, Hand Posture, Electromyography, Pattern recognition


Upper limb amputation is a significant limitation for achieving routine activities. Myoelectric signals detected by electrodes well-known as Electromyography (EMG) have been targeted to control upper limb prostheses of such lost limbs. Unfortunately, the acquisition, processing and use of such myoelectric signals are sophisticated. Furthermore, it necessarily requires complex computation to fulfil accuracy, robustness, and time-consumption execution for the real-time prosthesis application. Thus, machine learning schemes for pattern recognition are a potential approach to improve the traditional control for hand prostheses due to the movement of users and muscle contraction. This paper presents real-time hand posture recognition based on three hand postures using surface EMG (sEMG) signals. sEMG signals are acquired by the electrode channel and simultaneously collected while making a hand posture. Performance evaluation relies on classification accuracy and time consumption. The performance of six real-time recognition models is evaluated which combine two projection techniques and three classifiers. Results indicate that EMG-based pattern recognition (EMG-PR) control outperforms the traditional control for hand prostheses in real-time application. The highest classification accuracy is approximately 96%, whereas the lowest time consumption is 4 ms. In addition, the accuracy is dropped when the number of electrodes decreases nearly to 3%. These outcomes can apply to real-time hand prostheses to alleviate the limited prostheses available.


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How to Cite

Sae Jong, N., Sa-e, S., Tirasor, A. ., Mama, N., & Dao, H. (2023). HAND RECOGNITION SYSTEM BASED ON ELECTROMYOGRAPHY FOR REAL-TIME HAND PROSTHETIC CONTROL. ASEAN Engineering Journal, 13(1), 177–183. https://doi.org/10.11113/aej.v13.18641