Dynamic training protocol improves the robustness of PR-based myoelectric control

In pattern recognition (PR)-based myoelectric control schemes, the classifier is generally trained in ideal laboratory conditions, due to which the classification accuracy might be affected by confounding factors such as force variations, limb positions, and inadvertent electromyography (EMG) activation. Many endeavors have been put forward to mitigate this effect by adopting new training protocols that consider only quite a few independent factors. In this note, we propose a dynamic protocol, which embraces multiple EMG variations in data collection, to train a classifier with improved generalization ability. A total of four training protocols are examined, wherein affecting factors like upper-limb movements, contraction levels and inadvertent EMG activations are differently considered. Based on receiver operating characteristic (ROC) analysis, we came up with a new performance metric, ROC area rate (RAR), to directly inspect the accuracy and robustness of the classifiers obtained through different training protocols. Our results show that, compared with the other three protocols, the protocol with dynamic limb postures and dynamic muscle contractions (termed as DPDE) obtains the highest RAR (73.3%, on-way analysis of variance, p<0.005). Our results suggest that there is no need to integrate every EMG variation in the training protocol for receiving a robust EMG pattern recognition. Online control experiments with three amputees manipulating a multiple-DOF prosthetic hand also verify our findings.