Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion

Abstract

In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.

Fig. 1

Architecture of the neuromuscular–mechanical fusion system for locomotion mode recognition.

Fig. 2

Data windowing scheme and definition of gait phases in one stride cycle.

Fig. 3

Classification accuracy in the static state averaged over five TF amputees. The classification accuracies derived from EMG signals only (white bars), GRF/moments only (gray bars), and fusion of both data sources (black bars) using the SVM and LDA classifiers are shown for individual gait phases. “*” indicates a statistically significant difference (one-way ANOVA, P < 0.05).

Fig. 4

Example results of continuous-mode identification. (a) Results from a trial that recorded a transition between level-ground walking and stair ascent for subject TF01. (b) Results from a trial, where subject TF01 stepped over an obstacle during walking. Red vertical line indicates the critical timing t_c.