Muscle Classification Via Hybrid CNN-LSTM Architecture from Surface EMG Signals

Correct traceability of muscle identity within a predefined set of muscles in EMG studies is relevant in the periodic evaluation process of muscle training programs (for athletes), and in routine reviews for muscle rehabilitation. This article proposes hybrid deep learning CNN-LSTM models to classify the muscle directly from sEMG signals. These models allow for effective feature extraction and learning of short-term and long-term sequential dependencies. Two training setups are proposed: one using weight initialization provided from layer-wise unsupervised pretraining and another one using random initialization. Two validation scenarios are described to assess performance: testing on new contraction bursts from already-seen subjects in the training step (intrapersonal validation, useful in follow-up), and testing on a leave-one-out subject (interpersonal validation). Results indicate that the model can correctly classify different muscle groups in patients that already have been screened, but fails in distinguishing between symmetrical muscles.