Wearable interface for real-time gait phase recognition using sensor networks

Robust gait phase recognition is essential for gait analysis, biomechanical monitoring, and human-centered robotics. A significant gap persists between computational methods and field deployment spanning generalizability, computational budget, hardware optimization, and integration with robotic frameworks. This study presents a reproducible wearable system that bridges this gap by combining robust, real-time recognition with minimal modalities. The proposed probabilistic heuristic recognition algorithm for sequential events (PHRASE) leverages statistical modeling and artificial neural networks (ANN) to achieve robust low-latency detection. The system integrates two inertial measurement units (IMUs) and a portable microcomputer within a Robot Operating System (ROS) framework, enabling seamless integration with external systems. The method was validated against benchmarks on 31 participants with diverse biometrics, sensor models, and physical conditions during multi-scenario level-ground walking, outperforming 5 state-of-the-art deep learning benchmarks. PHRASE demonstrates strong generalization, achieving an accuracy of specifically across diverse unseen subjects, conditions, and experimental setups combined. The accuracy improvement over the best-performing benchmark has a 95% confidence interval of . The wearable interface maintains a stable average inference latency of 11.6 ms. Overall, the proposed interface addresses key limitations in terms of resilience to unseen subjects, unseen sensor configurations, varying walking speeds, and latency. The source code and implementation details are available at: https://github.com/SamMans/PHRASE/tree/main . • Real-time Bayesian gait phase recognition using only two wearable IMUs. • Portable ROS-based interface for assistive and robotic gait applications. • Combination of heuristics, artificial neural networks and Bayesian inference. • More robust and generalizable than deep learning benchmarks in cross-setup testing without retraining. • Faster than deep learning benchmarks in processing latency.