A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition
Type
Conference PaperKAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Estimation, Modeling and ANalysis Group
Date
2020-08-28Online Publication Date
2020-08-28Print Publication Date
2020-07Permanent link to this record
http://hdl.handle.net/10754/665147
Metadata
Show full item recordAbstract
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.Citation
Chahid, A., Khushaba, R., Al-Jumaily, A., & Laleg-Kirati, T.-M. (2020). A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc44109.2020.9176097Conference/Event name
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)ISBN
978-1-7281-1991-5Additional Links
https://ieeexplore.ieee.org/document/9176097/https://ieeexplore.ieee.org/document/9176097/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9176097
ae974a485f413a2113503eed53cd6c53
10.1109/EMBC44109.2020.9176097