Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human-robot coupled system, which conducts natural and close cooperation with the human by recognizing human locomotion timely. Requiring subject-specific training is the main challenge of the existing approaches, and most methods have the problem of insufficient recognition. This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition (LMR) method based on GA-CNN for a lower limb exoskeleton system. The LMR method is a combination of Convolutional Neural Networks (CNN) and Genetic Algorithm (GA)-based multi-sensor information selection. To improve network performance, the hyper-parameters are optimized by Bayesian optimization. An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method. Twelve locomotion modes, which composed an integral locomotion system for the daily application of the exoskeleton, can be recognized by the proposed method. According to a series of experiments, the recognizer shows strong comprehensive abilities including high accuracy, low delay, and sufficient adaption to different subjects.