The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients

Abstract

Objective: This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals.

Approach: Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions.

Main results: Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%).

Significance: The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.

Figure 1

Schematic description of the electrode placement for 41-channel surface EMG recordings.

Figure 2

Illustration of the 11 different wrist and hand functional movements used in this study.

Figure 3

Illustration of representative signal segments of a single surface EMG channel recorded from (a) Subject 3, (b) Subject 6, and (c) Subject 8, within (1) the slow session and (2) the fast session, respectively, when the subject was performing cylindrical grip. The gray rectangles under every signal segment mark voluntary muscle contractions. Each of the top two signal segments, (a1) and (a2), is also shown with an overview (top) and two expanded views (bottom) of voluntary and involuntary EMG activity, respectively.

Figure 4

Illustration of surface EMG onset/offset detection using sample entropy analysis, with signal segments recorded within (a) the slow session and (b) the fast session, respectively (the same as shown in Fig. 3a). The vertical arrows represent the detected onset/offset timing of voluntary contractions based on the sample entropy results in the bottom panel. The filtered EMG signal amplitude is also shown in the middle panel for comparison purpose.

Figure 5

Pattern-to-pattern confusion matrices derived from both two subjects using TD feature set under the Slow-Fast and Slow-Slow testing schemes, respectively. In each confusion matrix, an element at the x-th row and y-th column represents the number of testing windows for pattern x classified to pattern y. The main diagonal elements (shaded in dark) correspond to the numbers of correctly classified windows (i.e., TP) for each pattern, whereas other non-zero elements off the main diagonal are errors (FP or FN). Based on a confusion matrix, the Pre for each pattern is computed as the ratio of the diagonal element to the summation of all elements in the corresponding column, the Sen is computed as the ratio of diagonal element to the summation of all elements in the corresponding row, and the overall accuracy is computed as the ratio of the summation of all diagonal elements to the summation of all elements in the entire matrix.