Hand gesture recognition based on electromyography signals using artificial neural network

• Published in: I-manager's Journal on Image Processing Vol. 12; no. 2; p. 55
• Main Authors: Prasad, V. V. K. D. V., Mandava, Manikiran, Marneni, Likitha Susi, Kuricheti, Durga Venkata Manideep, Pamarthi, V N V Siva Ganesh
• Format: Journal Article
• Language: English
• Published: Nagercoil iManager Publications 01.06.2025
• Subjects: Adaptive technology; Artificial neural networks; Classification; Effectiveness; Electromyography; Entropy (Information theory); Gesture recognition; Human-computer interaction; Human-computer interface; Neural networks; Prostheses; Real time
• ISSN: 2349-4530; 2349-6827
• DOI: 10.26634/jip.12.2.21854

Hand gesture recognition plays a crucial role in human-computer interaction (HCI) and assistive technologies, particularly for individuals with motor impairments. In this study, an Artificial Neural Network (ANN) classifier is developed for recognizing hand gestures based on Electromyography (EMG) signals. The EMG dataset, consisting of multiple gesture classes, is preprocessed and normalized before being used to train an ANN model with two hidden layers. The model is trained using the Levenberg–Marquardt (trainlm) algorithm, with a cross-entropy loss function for multi-class classification. To evaluate the efficacy of the proposed ANN model, a comparative analysis was conducted against two traditional classifiers: Support Vector Classifier (SVC) and K-Nearest Neighbor (KNN). The experimental results show that the proposed ANN significantly outperforms the traditional classifiers. Additionally, the ANN demonstrates superior macro-averaged performance in terms of precision, recall, and F1-score, indicating its robustness and reliability across multiple gesture classes. These results demonstrate the effectiveness of ANN-based classification for EMG-based hand gesture recognition and highlight its potential for deployment in real-time prosthetic control, assistive technologies, and HCI systems.