Reference Frames in Human Sensory, Motor, and Cognitive Processing

Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus on motor behavior, more specifically on goal-directed reaching. We develop a model that explains how egocentric perceptual and motor reference-frames can be coordinated through exocentric reference-frames. Finally, in a third project, we study how the cognitive system can store and recognize objects by using sensorimotor schema that allows mental rotation within an exocentric reference-frame

A physics-based statistical model for human gait analysis

Physics-based modeling is a powerful tool for human gait analysis and synthesis. Unfortunately, its application suffers from high computational cost regarding the solution of optimization problems and uncertainty in the choice of a suitable objective energy function and model parametrization. Our approach circumvents these problems by learning model parameters based on a training set of walking sequences. We propose a combined representation of motion parameters and physical parameters to infer missing data without the need for tedious optimization. Both a κ-nearest-neighbour approach and asymmetrical principal component analysis are used to deduce ground reaction forces and joint torques directly from an input motion. We evaluate our methods by comparing with an iterative optimization-based method and demonstrate the robustness of our algorithm by reducing the input joint information. With decreasing input information the combined statistical model regression increasingly outperforms the iterative optimization-based method