A Wearable Device for Hand Sensorimotor Rehabilitation Through Augmented Sensory Feedback.

The loss of sensitivity of the upper limb due to central or peripheral neurological injuries severely limits the ability to manipulate objects, hindering personal independence. Non-invasive augmented sensory feedback techniques are used to promote neural plasticity hence to restore the grasping function. We devised a wearable device for hand sensorimotor rehabilitation capable of reliably detect transient tactile events based on custom piezoelectric polyvinylidene fluoride (PVDF) sensors and deliver discrete bursts of vibrations upon these events. We integrated the sensors into a fabric glove and tested the device in a pilot bench test exploring its ability to detect object contact and release as well as object slippage. Due to their broad bandwidth, the sensors proved to be suitable for both the applications: they responded with clear peaks when touching or releasing the object and increased the high-frequency content of the signal during slippage.

Looking for Synergies in Healthy Upper Limb Motion: A Focus on the Wrist.

Recent studies on human upper limb motion highlighted the benefit of dimensionality reduction techniques to extrapolate informative joint patterns. These techniques can simplify the description of upper limb kinematics in physiological conditions, serving as a baseline for the objective assessment of movement alterations, or to be implemented in a robotic joint. However, the successful description of kinematic data requires a proper alignment of the acquisitions to correctly estimate kinematic patterns and their motion variability. Here, we propose a structured methodology to process and analyze upper limb kinematic data, considering time warping and task segmentation to register task execution on a common normalized completion time axis. Functional principal component analysis (fPCA) was used to extract patterns of motion of the wrist joint from the data collected by healthy participants performing activities of daily living. Our results suggest that wrist trajectories can be described as a linear combination of few functional principal components (fPCs). In fact, three fPCs explained more than 85% of the variance of any task. Wrist trajectories in the reaching phase of movement were highly correlated among participants and significantly more than trajectories in the manipulation phase ( [Formula: see text]). These findings may be useful in simplifying the control and design of robotic wrists, and could aid the development of therapies for the early detection of pathological conditions.