It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors.

Ankle joint with contracture and/or spasticity can severely disable the mobility and the independence of stroke survivors. Robot-assisted rehabilitation has been proposed to support physicians in providing effective therapies. In this paper, we propose a robot-assisted ankle rehabilitation system integrated with human-computer interaction interface and acquisition of Electromyography signals, joint torque and joint angle. Furthermore, we investigate the effects of proprioceptive neuromuscular facilitation (PNF) rehabilitation method applied in our robotic system. The proposed robot-assisted system has been used in real experiments and provides PNF rehabilitation to five stroke patients for six weeks. Preliminary experimental results suggest that PNF is effective in increasing ankle range of motion(ROM), decreasing ankle resistance torque, and alleviating joint stiffness.

This paper presents a seven-link dynamic walking model that is more close to human beings. We add hip actuation, upper body, flat feet and compliant ankle joints to the model. Walking sequence of the flat-foot walker has several sub-streams that form bipedal walking with dynamic series of phases, which is different with the motion of round-foot and point-foot models. We investigate the characteristics of three different walking gaits with different step lengths. Comparison of these walking gaits in walking velocity, efficiency and stability reveals the relation between step length and walking performance. Experimental results indicate that the gait which is more close to human normal walking achieves higher stability and energetic efficiency.

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.