Citation

Hilman, Nashreen and Izni, Nor Aziyatul and Rahman, Mohd Azizi Abdul and Shapie, Mohamad Azlan Mohamed and Nasruddin, Fakhrizal Azmy (2026) Hybrid EMG-ECG Signal to Image Transformation Approach for Muscle Fatigue Reaction Classification. In: 16th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2026, 25 April 2026 - 26 April 2026, Hybrid, Penang.

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Abstract

The rise of sedentary lifestyles and chronic illness has contributed to healthcare challenges, particularly in the management of muscle fatigue. The existing studies solely focused on electromyography (EMG) in muscle fatigue investigation, and there have been no studies that propose a hybrid of EMG and ECG specifically for muscle response classification. Hence, the conventional approaches for analyzing EMG and ECG signals facing signal clarity and classification accuracy constraints, especially during dynamic movements. This study aims to develop a signal-to-image transformation algorithm that uses hybrid EMG-ECG signals in an image-based representations. By bargaining both spatial and temporal patterns that were collected through this transformation, the approach should improve the feature extraction and classification accuracy of the biosignals. The proposed method will minimize the need for complex preprocessing while improving noise robustness and dimensionality reduction. This approach has the potential to significantly improve muscle reaction classification compared to existing methods, enabling more effective, personalized rehabilitation and advanced muscle fatigue monitoring.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Electromyography, Electrocardiograph
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 07:06
Last Modified:	04 Aug 2026 07:06
URII:	http://shdl.mmu.edu.my/id/eprint/16499

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