Resolving the adverse impact of mobility on myoelectric pattern recognition in upper-limb multifunctional prostheses

Electromyogram pattern recognition (EMG-PR) based control for upper-limb prostheses conventionally focuses on the classification of signals acquired in a controlled laboratory setting. in such a setting, relatively stable and high performances are often reported because subjects could consistently perform muscle contractions corresponding to a targeted limb motion. Meanwhile the clinical implementation of EMG-PR method is characterized by degradations in stability and classification performances due to the disparities between the constrained laboratory setting and clinical use. One of such disparities is the mobility of subject that would cause changes in the EMG signal patterns when eliciting identical limb motions in mobile scenarios. In this study, the effect of mobility on the performance of EMG-PR motion classifier was firstly investigated based on myoelectric and accelerometer signals acquired from six upper-limb amputees across four scenarios. Secondly, three methods were proposed to mitigate such effect on the EMG-PR motion classifier. From the obtained results, an average classification error (CE) of 9.50% (intra-scenario) was achieved when data from the same scenarios were used to train and test the EMG-PR classifier, while the CE increased to 18.48% (inter-scenario) when trained and tested with dataset from different scenarios. This implies that mobility would significantly lead to about 8.98% increase of classification error (p < 0.05). By applying the proposed methods, the degradation in classification performance was significantly reduced from 8.98% to 1.86% (Dual-stage sequential method), 3.17% (Hybrid strategy), and 4.64% (Multi scenario strategy). Hence, the proposed methods may potentially improve the clinical robustness of the currently available multifunctional prostheses.

基于肌电图模式识别（EMG - PR）的上肢假肢控制传统上侧重于在受控实验室环境中采集的信号分类。在这种环境下，经常报道有相对稳定和高性能的结果，因为受试者能够始终如一地进行与目标肢体运动相对应的肌肉收缩。同时，由于受限的实验室环境和临床使用之间的差异，EMG - PR方法在临床应用中的特点是稳定性和分类性能下降。其中一种差异是受试者的移动性，在移动场景中引发相同肢体运动时，这会导致肌电信号模式发生变化。在本研究中，首先基于从六个上肢截肢者在四种场景下采集的肌电和加速度计信号，研究了移动性对EMG - PR运动分类器性能的影响。其次，提出了三种方法来减轻这种对EMG - PR运动分类器的影响。从所得结果来看，当使用来自相同场景的数据训练和测试EMG - PR分类器时，平均分类误差（CE）达到9.50%（场景内），而当使用来自不同场景的数据集进行训练和测试时，CE增加到18.48%（场景间）。这意味着移动性将显著导致分类误差增加约8.98%（p < 0.05）。通过应用所提出的方法，分类性能的下降从8.98%显著降低到1.86%（双阶段顺序方法）、3.17%（混合策略）和4.64%（多场景策略）。因此，所提出的方法可能潜在地提高当前可用的多功能假肢的临床稳健性。