Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only

Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, this study was to suggest an identification algorithm for transitions between walking environments based on the entire EMG signals of selected lower extremity muscles using a deep learning approach. The muscle activations of the rectus femoris, vastus medialis and lateralis, semitendinosus, biceps femoris, tibialis anterior, soleus, medial and lateral gastrocnemius, flexor hallucis longus, and extensor digitorum longus of 27 subjects were measured while walking on flat ground, upstairs, downstairs, uphill, and downhill and transitioning between these walking surfaces. An artificial neural network (ANN) was used to construct the model, taking the entire EMG profile during the stance phase as input, to identify transitions between walking environments. The results show that transitioning between walking environments, including continuously walking on a current terrain, was successfully classified with high accuracy of 95.4 % when using all muscle activations. When using a combination of muscle activations of the knee extensor, ankle extensor, and metatarsophalangeal flexor group as classifying parameters, the classification accuracy was 90.9 %. In conclusion, transitioning between gait environments could be identified with high accuracy with the ANN model using only EMG signals measured during the stance phase.