Investigating motor adaptation to stable and unstable tasks using haptic interfaces, EMG and fMRI

Our talk describes our general project, i.e. how we investigate the adaptation to stable and unstable tasks using EMG, fMRI and haptic interfaces. This short paper focuses on a spin-off. It describes a simple computational model of force and impedance in human arm movements, which can be used to simulate human motion and to improve the control of human-machine interfaces. This model, based on recent physiological findings, assumes that a) the central nervous system learns the force and impedance to perform the task successfully in a given stable or unstable dynamic environment; b) for stable interaction, stiffness is linearly related to the magnitude of the joint torque. Comparison with existing data shows that this simple model is able to predict impedance geometry well.