A strategy for identifying locomotion modes using surface electromyography

Abstract

This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% +/- 5.0%, 6.0% +/- 4.7%, 7.5% +/- 5.1%, and 5.2% +/- 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.

Fig. 1

(a) Architecture of phase-dependent EMG pattern classifier. (b) Four defined phase windows aligned with heel contact (HC) and toe-off (TO). Raw EMG signals in one stride cycle are demonstrated.

Fig. 2

Classification performance of the LDA and the ANN classifiers. The classification errors were averaged over eight able-bodied subjects. The results derived from the phase-dependent PR and the full-stride PR design are demonstrated. * demonstrates statistically significant difference (one-way ANOVA, P < 0.05). In this test, the TD features, 16 EMG channels, 140-ms windows, and 30-ms window increments were used.

Fig. 3

Influence of EMG features and length of the windows on classification error of LDA. The averaged classification error over eight subjects is demonstrated in each defined gait phase. The size of the window increment was set to 30 ms.

Fig. 4

Influence of window increment size on classification error. The error averaged over eight subjects is shown in each defined gait phase. A 140-ms analysis window and TD EMG features were applied. The P-values derived from a one-way ANOVA are presented above individual phases.

Fig. 5

Comparison of classification error averaged over eight able-bodied subjects using different amounts of neuromuscular control information. Combination 1 (black bars): the classification error found using all recorded 16 EMG channels; combination 2 (dark gray bar): the error found using 10 EMG channels above the knee; combination 3 (light gray bars): the error found using EMG of thigh muscles only; combination 4 (white bars): the error found using EMG from muscles in shank and foot.

Fig. 6

Classification error of each task mode for data recorded from subjects (a) TF1 and (b) TF2. (Black bars) Classification error of each task found using all 11 channels of EMG. (White bars) Classification error of each task found using EMG signals in the residual limb only (nine channels). * denotes 0% classification error.

Fig. 7

Bar chart of the confusion matrix for mode classification averaged over eight able-bodied subjects in each phase using EMG from ten muscles above the knee. TD features, 140-ms window length, and 30-ms window increments were used. At the interception of the estimated task j and the targeted task i, a bar is demonstrated, and its height maps the value of element in the confusion matrix. The diagonal values of the matrix, representing the average classification accuracy rate for individual tasks, are shown above the diagonal bars.