BHIS: A Bayesian–Heuristic Inference System for Recognition of Walking Gait Phases

Gait phase recognition is vital for advancing assistive robotics, enabling phase-based assistance throughout the gait cycle. This article presents a real-time method using wearable sensors and computational methods for classifying the seven gait subphases. Current methods often struggle with accuracy on unseen subjects. Furthermore, walking speed variability, hardware complexity, and response time hinder robustness, portability, and real-time performance, respectively. A Bayesian method constructs posterior belief by selecting likely phase transition candidates heuristically and combining biomechanical signal knowledge with pattern recognition techniques. The approach is validated and benchmarked against prevailing deep learning (DL) methods using two datasets, each containing data from two inertial measurement units (IMUs) attached to the midshanks of test subjects. The first dataset includes six participants, while the second dataset includes ten, all walking at their comfortable speeds. Additionally, the method is validated in real time for nine test subjects walking at varying speeds (2.2–3.5 mph). The proposed method demonstrates strong robustness, achieving average steady accuracies of 98% and 97.4% for seen and unseen subjects, respectively, with an average runtime of 1.8 ms.