Wrist angle estimation under different loads condition based on multi-layer perceptron neural network and surface electromyography signals

Name: Songpon Pumjam

Organization : King Mongkut’s University of Technology North Bangkok. Faculty of Technical Education. Department of Teacher Training in Electrical Engineering

Name: Sarut Panjan

Organization : King Mongkut’s University of Technology North Bangkok. Faculty of Technical Education. Department of Teacher Training in Electrical Engineering

Email : sarutp@kmutnb.ac.th

Name: Tarinee Tonggoed

Organization : King Mongkut’s University of Technology North Bangkok. Faculty of Technical Education. Department of Teacher Training in Mechanical Engineering

Email : tarinee.t@fte.kmutnb.ac.th

Name: Anan Suebsomran

Organization : King Mongkut’s University of Technology North Bangkok. Faculty of Technical Education. Department of Teacher Training in Mechanical Engineering

Email : anan.s@fte.kmutnb.ac.th

keyword: Surface electromyography

LCSH: Electromyography

; Multilayer perceptrons

LCSH: Forearm

LCSH: Human mechanics

LCSH: Neural networks (Computer science)

LCSH: Machine learning

LCSH: Artificial intelligence

Abstract: This research focuses on the impact of load on wrist angle prediction using surface electromyographic (sEMG) signals from forearm muscles, specifically considering wrist flexion-extension. The goal is to develop a model capable of accurately predicting wrist angles under various conditions. A multilayer perceptron neural network (MLPNN) was constructed and tested, processing features extracted from the mean absolute value (MAV) of sEMG signals. The experiments included diverse simulations of load to examine the model’s responsiveness to these forces. The findings indicate that the model can reliably predict wrist angles, as evidenced by high R-squared values and low mean squared error (MSE) rates. This study suggests the potential for developing high-precision tools for human posture analysis and prediction that could be applied in various situations, including research and potentially physical therapy and prosthetic technologies in the future.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2024

Modified: 2026-06-02

Issued: 2026-06-02

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BibliograpyCitation : In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI) Association, Thailand. 2024 21th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2024) (pp.103-108). Piscataway, NJ : IEEE

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