Subject-Independent Data Pooling in Classification of Gait Intent Using Mechanomyography on a Transtibial Amputee

Active lower limb prosthetics rely on the detection of gait mode to direct controller response. The majority of systems require feedback from the prosthetic and/or inertial measurement units (IMUs). Reliance on movement delays classification, reducing the range of patient activities and terrain traversed. Neuromuscular interfaces using electromyography (EMG) enable real-time monitoring by registering user intent, however EMG has known robustness issues out-of-clinic that have impeded its translation. Furthermore, supervised training of gait classifiers can require large subject-specific amputee data sets which are difficult to obtain. Mechanomyography (MMG) has shown less dependence on environmental conditions than EMG yet has seen limited use in this realm. In this investigation we introduce an MMG gait classifier targeting improved control of prosthetic (robotic) legs. We compare the accuracy of subject specific classifiers to those trained using subject-independent pooling. Additionally, we quantify the effect of introducing a small amount of data from individual test subjects to the training pool. Experiments were performed on 12 participants and 5 gait modes. A support vector machine (SVM) classifier achieved 65% accuracy with subject-specific data, 92% with pooled training data, and 94% with pooled plus limited user-specific data. The results show the promise of MMG gait classifiers with increased robustness and reduced subject-specific training in prosthetic control.