Factored Interval Particle Filtering for Gait Analysis

Scheduled for presentation during the Poster Session "Signal Pattern Classification in Biomedical Signals V" (FrP2A1)International audienceCommercial gait analysis systems rely on wearable sensors. The goal of this study is to develop a low cost marker less human motion capture tool. Our method is based on the estimation of 3d movements using video streams and the projection of a 3d human body model. Dynamic parameters only depend on human body movement constraints. No trained gait model is used which makes this approach generic. The 3d model is characterized by the angular positions of its articulations. The kinematic chain structure allows to factor the state vector representing the conguration of the model. We use a dynamic bayesian network and a modied particle filtering algorithm to estimate the most likely state conguration given an observation sequence. The modied algorithm takes advantage of the factorization of the state vector for efciently weighting and resampling the particles

Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks

International audienceParticle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distributions. The dimensionality of the problem remains a limitation of these approaches due to the growing number of particles required for the exploration of the state space. Computer vision problems such as 3D motion tracking are an example of complex monitoring problems which have a high dimensional state space and observation functions with high computational cost. In this article we focus on reducing the required number of particles in the case of monitoring tasks where the state vector and the observation function can be factored. We introduce a particle filtering algorithm based on the dynamic Bayesian network formalism which takes advantage of a factored representation of the state space for efficiently weighting and selecting the particles. We illustrate the approach on a simulated and a realworld 3D motion tracking tasks