Incremental Classification for Myoelectric Manifold Representation With Matrix-Formed Growing Neural Gas Network

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 8; pp. 10065 - 10073
• Main Authors: Ding, Qichuan, Yin, Peng, Ai, Jinshuo, Han, Shuai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Electrodes; Electromyography; Feature extraction; Feature recognition; Gesture recognition; Growing neural gas (GNG) network; human–robot interaction (HRI); incremental classification; Manifolds; Matrices (mathematics); Muscles; Myoelectricity; Prototypes; Representations; Signal classification; surface electromyography (sEMG); symmetric positive definite (SPD) manifold; Time-frequency analysis; Topology; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3393004

Current surface electromyography (sEMG)-based gesture recognition only extracts time or frequency features from raw sEMG signals, and then puts the features together to generate sample vectors, which are further used as inputs to build fixed classification models. This way may bring out two issues. First, raw sEMG signals are often acquired from multichannel electrodes. Only extracting time or frequency features will lose the spatial topology information between different channels, and cannot reflect the movement synergy of different muscles, causing relatively low recognition accuracies. Second, fixed classifiers only recognize fixed gestures, and cannot handle dynamically increasing gestures, limiting the scalabilities of classifiers in applications. To this end, we introduce a myoelectric manifold representation based on the symmetric positive definite (SPD) matrix to express the spatial synergy of multiple muscles. Then, the growing neural gas network is extended to the SPD manifold space, and uses myoelectric matrices as inputs to realize the incremental gesture recognition, maintaining the space topology with very few prototypes. Extensive experiments were conducted on two public databases (Ninapro DB2 and DB5) and a self-collection database. Experimental results showed that our method was superior to current methods, increasing recognition accuracy by 1.63%-11.89%, and can continuously grow its recognition ability online, revealing the potential in implementing myoelectric interaction systems.