Real-Time Data-Driven Gait Phase Detection Using Infinite Gaussian Mixture Model and Parallel Particle Filter

The world is experiencing an unprecedented enduring, and pervasive aging process. With more people who need walking assistance, the demand for gait rehabilitation has increased rapidly over the years. Effective gait rehabilitation requires a comprehensive gait analysis, in which gait phase detection plays an important role. Although many specialized sensing systems have been developed for gait monitoring, most existing gait phase detection algorithms rely on significant input from medical professionals, which are subjective, manual and inaccurate. To address these problems, this paper presents a datadriven approach for real-time gait phase detection. The approach combines an infinite Gaussian mixture model (IGMM) to classify different gait phases based on the ground contact force (GCF) measurement, and a parallel particle filter to estimate and update the model parameters. Effective particle sharing mechanisms are further designed to distribute particles among different working nodes judiciously and thus strike a good balance between computational overhead and estimation accuracy. The proposed algorithm is implemented in our gait monitoring and analysis platform developed on Microsoft Azure, and examined using the data trace collected from a healthy human subject. The algorithm effectiveness is validated through extensive experiments.