CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
J Neural Eng. 2023 Apr 3;20(2). doi: 10.1088/1741-2552/acc42a.
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.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.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%.. 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.. 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.
假体系统用于提高截肢后患者的生活质量，基于表面肌电图 (sEMG) 的手势分类研究已经取得了丰富的成果。然而，目前的手势分类算法主要关注同一对象，而克服生理因素的跨个体分类研究相对较少，导致临床假体系统的弃用率较高。本研究旨在提出一种能够显著提高跨个体手势分类准确性的算法。
招募了 8 名健康成年人，记录了他们进行 7 种日常手势的 sEMG 数据。提出了一种改进的模糊粒度化逻辑回归 (FG_LogR) 算法，用于跨个体手势分类。
结果表明，基于 FG_LogR 算法的 4 种特征的平均分类准确率分别为 79.7%、83.6%、79.0%和 86.1%，而基于逻辑回归算法的分类准确率分别为 76.2%、79.5%、71.1%和 81.3%，整体准确率提高了 3.5%到 7.9%。FG_LogR 算法的性能也优于其他五种经典算法，平均预测准确率提高了 5%以上。
所提出的 FG_LogR 算法通过模糊和粒度化特征提高了跨个体手势识别的准确性，具有临床应用的潜力。本研究提出的算法有望与其他特征优化方法相结合，实现更精确和智能的假肢控制，解决假肢系统手势识别不佳和弃用率高的问题。
