Improving Long Term Myoelectric Decoding, Using an Adaptive Classifier with Label Correction

Abstract

This study presents a novel adaptive myoelectric decoding algorithm for control of upper limb prosthesis. Myoelectric decoding algorithms are inherently subject to decay in decoding accuracy over time, which is caused by the changes occurring in the muscle signals. The proposed algorithm relies on an unsupervised and on demand update of the training set, and has been designed to adapt to both the slow and fast changes that occur in myoelectric signals. An update in the training data is used to counter the slow changes, whereas an update with label correction addresses the fast changes in the signals. We collected myoelectric data from an able bodied user for over four and a half hours, while the user performed repetitions of eight wrist movements. The major benefit of the proposed algorithm is the lower rate of decay in accuracy; it has a decay rate of 0.2 per hour as opposed to 3.3 for the non adaptive classifier. The results show that, long term decoding accuracy in EMG signals can be maintained over time, improving the performance and reliability of myoelectric prosthesis.

Fig. 1:

The figures show the plot of time versus a particular dimension in which the classifier can seperate the data. The figure shows the two types of concept drift, figure 1a shows the change in the distribution from t=0 where the classifier was trained to t=T when the classifier is used to make a prediction. As a result of slow concept drift, the data has moved but can still be classified correctly, as well as be used to train a new classifier as we have the correct labels avaialable. In figure 1b demonstrates fast concept drift where the data moves a considerable distance and part of the data is missclassified and hence cannote be used to train a new classifier. Both the images are created using simulated data, using unimodal gaussian distributions.

Fig. 2:

The figure shows the use of the Teager Keiser Energy Operator to detect movement onset and offset. The Raw signal after filtering, the signal after applying the TKEO and the signal after further low pass filtering are shown. As can be seen from the figure there is a delay caused due to response time of the subject which needs to be accounted for as well as the a delay caused by the TKEO based detection scheme.

Fig. 3:

Block Diagram of the Proposed System.

Fig. 4:

The figures show the key results of the proposed algorithm, 4a is the plot of the mean accuracy over a session versus the session after supervised training. It shows the improvement due to the adaptive system can be seen from the significant difference between the two methods towards the end. 4b shows the plot of the mean accuracy of the training set (the accuracy of the data used to create the classifier). The improvement in the accuracy of the training set over the sessions shows the reason why the decoding accuracy is higher for the proposed algorithm.