Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

直观的假肢控制是降低用户学习假手使用难度的核心挑战之一。本研究提出一种全新的表面肌电信号（surface electromyography, sEMG）模式识别方法，可通过少量电极精准识别灵巧手部与手指的运动，这将提升截肢患者的使用信心与易用性。

本系统针对10名前臂截肢患者开展评估，并将结果与健康受试者的表现进行对比。研究分析了基于分形分析的多种sEMG特征，包括去趋势波动分析（detrended fluctuation analysis）与Higuchi分形维数（Higuchi’s fractal dimension），并结合传统的基于幅值的特征。采用遗传算法与序列前向选择法筛选最优特征集。分别使用支持向量机（Support Vector Machine, SVM）、K近邻（K-nearest neighbors, KNN）与线性判别分析（Linear Discriminant Analysis, LDA）三类分类器，基于4个电极采集的信号，对单根手指屈曲、手部动作与不同握持姿态进行分类，受试者以自然方式完成肌肉收缩以执行各类任务。针对健康受试者与截肢患者两组人群，采用不同特征集的所有分类方法均进行了统计学显著性检验。

结果显示，使用SVM时健康受试者的平均分类准确率可达99.2%，截肢患者可达98.94%，KNN的准确率紧随其后。不过KNN的计算复杂度更低，这使其在实时应用中更具优势。

本研究提出的方法在精准控制灵巧假肢手方面颇具应用前景，可为截肢患者提供更多功能与更高的接受度。