A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

Abstract

Abstract Objective. Prosthetic systems are used to improve the quality of life of post-amputation patients, and research on surface electromyography (sEMG)-based gesture classification has yielded rich results. Nonetheless, current gesture classification algorithms focus on the same subject, and cross-individual classification studies that overcome physiological factors are relatively scarce, resulting in a high abandonment rate for clinical prosthetic systems. The purpose of this research is to propose an algorithm that can significantly improve the accuracy of gesture classification across individuals. Approach. Eight healthy adults were recruited, and sEMG data of seven daily gestures were recorded. A modified fuzzy granularized logistic regression (FG_LogR) algorithm is proposed for cross-individual gesture classification. Main results. The results show that the average classification accuracy of the four features based on the FG_LogR algorithm is 79.7%, 83.6%, 79.0%, and 86.1%, while the classification accuracy based on the logistic regression algorithm is 76.2%, 79.5%, 71.1%, and 81.3%, the overall accuracy improved ranging from 3.5% to 7.9%. The performance of the FG_LogR algorithm is also superior to the other five classic algorithms, and the average prediction accuracy has increased by more than 5%. Conclusion. The proposed FG_LogR algorithm improves the accuracy of cross-individual gesture recognition by fuzzy and granulating the features, and has the potential for clinical application. Significance. The proposed algorithm in this study is expected to be combined with other feature optimization methods to achieve more precise and intelligent prosthetic control and solve the problems of poor gesture recognition and high abandonment rate of prosthetic systems.