Implementing dual quaternions as a basis for sensor fusion

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Sensor fusion is the process by which (potentially redundant) information from different sensory sources is combined. In pose estimation, data can arise from optical imaging or inertial sensors, among others. Since the data is measured in different reference frames, it is not trivial to combine (fuse) them within a rigid body skeletal model to obtain instantaneous pose. Here, we propose a novel sensor fusion approach using Bayesian principles, Kalman filters and dual quaternions to obtain optimal estimates of pose from different sensors. This approach will be developed in collaboration with HAS-Motion and implemented in their Visual3D software. We will also record walking/running data to test our new algorithm against existing procedures. This will provide critically needed sensor fusion technology for biomechanical assessment and rehabilitation.