A Hybrid Bayesian-Heuristic Inference System for Recognition of Gait Phase

Recognition of gait phases holds great significance in the advancement of assistive robotic technologies. Assistive devices rely on phase-based control for automatic support throughout the gait cycle. Wearable sensors are instrumental in achieving gait phase recognition by providing streams of rich raw data. This paper focuses on the classification of the seven phases of the gait cycle by addressing key challenges in the literature. Existing classification approaches exhibit limited accuracy when applied to unseen data from unseen test subjects, highlighting robustness challenges. Portability and real-time performance are also impacted by challenges in hardware complexity and classifier response time. This paper introduces a hybrid approach that establishes a posterior belief by combining prior biomechanical signal knowledge, heuristics, and pattern recognition. The presented method is validated and benchmarked against Short-Long-Term-Memory (LSTM) model using an open-source dataset consisting of kinematic feedback from 4 inertial measurement units (IMU) attached to the lower limbs of 6 healthy subjects. The proposed approach demonstrates high robustness, achieving average online accuracy of 98.69% and 97.94% for seen and unseen subjects, respectively, with an average run time of 4 ms.