Phantom: Prediction of Human Motion with Distributed Body Sensors

Tracking human motion with distributed body sensors has the potential to promote a large number of applications such as health care, medical monitoring, and sports medicine. In distributed sensory systems, the system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and complementary applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a distributed sensory system with multi-channel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization (EM) algorithm for distributed sensors, we can estimate the interactive relationship of multiple sensor channels and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with improved control of filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement as well as the velocity from measured datasets with distributed sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results showed that the prediction overshoot error can be improved in the average 19.31 % with employing a tracking relationship.