Human gait-type recognition without pre-training: an adaptive fuzzy-based approach for locomotion-assistance devices

Gait-type recognition is important for robotic exoskeletons and walking-assistance devices to adjust their output according to the users’ needs. However, the growing trend of using machine learning (ML) models is both labor- and data-intensive, which makes it practically less attractive for application in exoskeletons and wearable-assistive devices. This research aims to devise a fuzzy-based gait recognition algorithm that requires minimum training data (only 40 cycles for each of the 5 gait types) and adapts to new users without having the need of pre-training for each of them. The proposed algorithm uses the fuzzy logic system (FLS) and Welford’s (variance computation) method to enhance the adaptability by adjusting the rules for gait-type recognition and fine-tuning them in real time for every new user without requiring a specific prior training. Simulation-based evaluation of the proposed algorithm shows a gait-type recognition accuracy of 63.0%, an improvement of 36.8% over the non-adaptive fuzzy-based recognition algorithm. Moreover, the results show that the proposed algorithm outperforms the popular ML methods (support vector machine, Naive Bayes classifier, and logistic regression) when subjected to limited gait-cycles data and no prior training is provided.