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2024 47th International Conference on Telecommunications and Signal Processing (TSP)

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. ResNeXt, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). ResNeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEMG and goniometer signals, aiding early disease detection and treatment.

Sakorn Mekruksavanich
School of Information and Communication Technology, University of Phayao
Thailand

Wikanda Phaphan
King Mongkut’s University of Technology North Bangkok
Thailand

Anuchit Jitpattanakul
King Mongkut’s University of Technology North Bangkok
Thailand

 

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