A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition

• Published in: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society pp. 5765 - 5768
• Main Authors: Chahid, Abderrazak, Khushaba, Rami, Al-Jumaily, Adel, Laleg-Kirati, Taous-Meriem
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• Subjects: electromyography; EMG; feature extraction; hand gesture; position weight matrix
• ISSN: 1558-4615
• DOI: 10.1109/EMBC44109.2020.9176097

Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals. This method is validated on surface Electromyogram (sEMG) signals recognition for eight different hand gestures. The used CapgMyo dataset consists of high-density sEMG signals across 128 channels acquired from 9 intact subjects. Our pilot results show that an accuracy of up to 83% can be achieved for some subjects using a support vector machine classifier, and an average accuracy of 75% has been reached for all studied subjects using the CapgMyo dataset. The proposed method shows a good potential in extracting relevant features from different biomedical signals such as Electroencephalogram (EEG) and Magnetoencephalogram (MEG) signals.