Investigation of adaptive muscle synergy modulated motor responses to grasping perturbations.

This study investigated how muscle synergies adapt in response to unexpected changes in object weight during lifting tasks. The aim was to discover which motor control strategies individuals use to maintain their grasping performance. Muscle synergies were extracted from the muscle activity of fifteen healthy participants who lifted objects of identical appearance but varying weights in a randomized order, which introduced artificial perturbations. Reaching and manipulation phases of object lifting were analyzed using constrained non-negative matrix factorization and k-means clustering. Participants exhibited a perturbation-independent and thus consistent recruitment of spatial synergy components, while significant adaptations in muscle synergy activation occurred in response to unexpected perturbations. Perturbations caused by unexpectedly heavy objects led to delayed and gradual increases in muscle synergy activation until the force required to lift the object was reached. In contrast, perturbations caused by lighter objects led to reductions in excess muscle synergy activation occurring later. Sensorimotor control maintains the modularity of muscle synergies. Even when external mechanical perturbations occur, the grasping performance is preserved, and control is adapted solely through muscle synergy activation. These results suggest that using pure spatial synergy components as control signals for myoelectric arm prostheses may prevent them from malfunctioning due to external perturbations.

Tactile Feedback in Upper Limb Prosthetics: A Pilot Study on Trans-Radial Amputees Comparing Different Haptic Modalities.

Despite technological advancements, upper limb prostheses still face high abandonment/rejection rates due to limitations in control interfaces and the absence of force/tactile feedback. Improving these aspects is crucial for enhancing user acceptance and optimizing functional performance. This pilot study, therefore, aims to understand which sensory feedback in combination with a soft robotic prosthetic hand could provide advantages for amputees, including performing everyday tasks. Tactile cues provided are contact information, grasping force, degree of hand opening, and combinations of this information. To transfer such feedback, different wearable systems are used, based on either vibrotactile or force stimulation in a non-invasive modality matching approach. Five volunteers with a trans-radial amputation controlling the new prosthetic hand SoftHand Pro performed a study protocol including everyday tasks. The results indicate the preference of amputees for a single, i.e. non-combined, feedback modality. The choice of appropriate haptic feedback seems to be subject and task-specific. Furthermore, in alignment with the participants' feedback, force feedback, with adequate granularity and clarity, could potentially be the most valuable feedback among those presented. Finally, the study suggests that prosthetic solutions should be preferred where amputees are able to choose their feedback system.

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions.

BACKGROUND: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.