Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes

Pattern recognition of myoelectric signals in upper-limb prosthesis control has been subject to intense research for several years. However, few systems have yet been successfully clinically implemented. One possible explanation for this discrepancy is that published reports mostly focus on classification accuracy of myoelectric signals recorded under laboratory conditions as the metric for the system's performance. These data are usually acquired only during the static state of the contraction in a fixed seated position. This supports the test subject in performing repeatable contractions throughout the experiment and generally results in an unrealistically high classification accuracy. In clinical testing however, subjects have to perform various activities of daily living, causing the limb to move in different positions. These variations in limb positions can significantly decrease robustness and usability of myoelectric control systems. Recent reports have shown that the so-called limb position effect can be resolved for the static state of the signal by adding accelerometer data to the feature vector. Including data from the transient state of the signals for classifier training generally significantly increases the classification error so it is mostly not considered in published reports. In this paper, we investigate the classification accuracy of transient EMG data, taking into account the limb position effect. We demonstrate that a classifier trained with features from EMG, accelerometer and gyroscope outperforms classifiers using only EMG or EMG and accelerometer data when classifying transient EMG data.