Distributed Estimation of Linear Acceleration for Improved Accuracy in Wireless Inertial Motion Capture

Motion capture using wireless inertial measurement units (IMUs) has many advantages over other techniques. Achieving accurate tracking with IMUs presents a processing challenge, especially for real time tracking. Centralised approaches are bandwidth-intensive and prone to error from packet loss. Methods based solely on local knowledge have poor dynamic accuracy, due to ambiguities introduced by linear acceleration. First we analyse the effect of linear acceleration on orientation accuracy. We then present an efficient distributed method which uses a model of the subject's body structure to estimate and correct for linear acceleration. We validate the behaviour of this method on data from combined optical/inertial capture experiments, and show improved gravity vector estimation and a corresponding increase in orientation accuracy. We estimate the runtime, memory, communication and power requirements of our method, and show that it is a practical software modification to an existing system. The proposed solution is the first to use collaboration between wireless IMUs to improve accuracy.