Online kinematic regulation by visual feedback for grasp versus transport during reach-to-pinch

Reach-to-grasp kinematics and kinetics with and without visual feedback in early-stage Alzheimer’s disease

This study aimed to investigate the effects of early-stage Alzheimer’s disease (AD) on the reach-to-grasp kinematics and kinetics with and without visual supervision of the grasping arm and hand. Seventeen patients who had been diagnosed with early-stage AD and 17 age- and gender-matched, cognitive normal (CN) adults participated in the experiment. A mirror operating system was designed to block the visual feedback of their grasping hand and forearms but to virtually show grasped targets. The target for reach-to-grasp kinematics was a reflective marker installed on a base; and the target for reach-to-grasp kinetics was a custom-made apparatus installed with two six-component force/torque transducers. Kinematics and kinetic parameters were used to quantify the reach-to-grasp performances. Results showed that the early-stage AD remarkably decreased the reaching speed, reduced the grasping accuracy and increased the transportation variability for reach-to-grasp kinematics. For kinetic analysis, early-stage AD extended the preload duration, disturbed the grip and lift forces coordination, and increased the feedforward proportion in the grasping force control. The AD-related changes in the reach-to-grasp kinematic and kinetic parameters depended on visual feedback and were associated with nervous system function according to correlation analyses with the neuropsychological testing. These results suggest that the abnormal kinematic and kinetic characteristics may correlate with the neuropsychological status of early-stage AD, and that the reach-to-grasp kinematic and kinetic maneuver could potentially be used as a novel tool for non-invasive screening or evaluation of early-stage AD.

Agency and Performance of Reach-to-Grasp With Modified Control of a Virtual Hand: Implications for Rehabilitation

This study investigated how modified control of a virtual hand executing reach-to-grasp affects functional performance and agency (perception of control). The objective of this work was to demonstrate positive relationships between reaching performance and grasping agency and motivate greater consideration of agency in movement rehabilitation. We hypothesized that agency and performance have positive correlation across varying control modes of the virtual hand. In this study, each participant controlled motion of a virtual hand through motion of his or her own hand. Control of the virtual hand was modified according to a specific control mode. Each mode involved the virtual hand moving at a modified speed, having noise, or including a level of automation. These specific modes represent potential control features to adapt for a rehabilitation device such as a prosthetic arm and hand. In this study, significant changes in agency and performance were observed across the control modes. Overall, a significant positive relationship (p < 0.001) was observed between the primary performance metric of reach (tracking a minimum path length trajectory) and an implicit measurement of agency (intentional binding). Intentional binding was assessed through participant perceptions of time-intervals between grasp contact and a sound event. Other notable findings include improved movement efficiency (increased smoothness, reduced acceleration) during expression of higher agency and shift toward greater implicit versus explicit agency with higher control speed. Positively relating performance and agency incentivizes control adaptation of powered movement devices, such as prostheses or exoskeletons, to maximize both user engagement and functional performance. Agency-based approaches may foster user-device integration at a cognitive level and facilitate greater clinical retention of the device. Future work should identify robust and automated methods to adapt device control for increased agency. Objectives include how virtual reality (VR) may identify optimal control of real-world devices and assessing real-time agency from neurophysiological signals.

Characterization of the Stroke-Induced Changes in the Variability and Complexity of Handgrip Force

Introduction: The variability and complexity of handgrip forces in various modulations were investigated to identify post-stroke changes in force modulation, and extend our understanding of stroke-induced deficits. Methods: Eleven post-stroke subjects and ten age-matched controls performed voluntary grip force control tasks (power-grip tasks) at three contraction levels, and stationary dynamometer holding tasks (stationary holding tasks). Variability and complexity were described with root mean square jerk (RMS-jerk) and fuzzy approximate entropy (fApEn), respectively. Force magnitude, Fugl-Meyer upper extremity assessment and Wolf motor function test were also evaluated. Results: Comparing the affected side with the controls, fApEn was significantly decreased and RMS-jerk increased across the three levels in power-grip tasks, and fApEn was significantly decreased in stationary holding tasks. There were significant strong correlations between RMS-jerk and clinical scales in power-grip tasks. Discussion: Abnormal neuromuscular control, altered mechanical properties, and atrophic motoneurons could be the main causes of the differences in complexity and variability in post-stroke subjects.

Learning to grasp and extract affordances: the Integrated Learning of Grasps and Affordances (ILGA) model

The activity of certain parietal neurons has been interpreted as encoding affordances (directly perceivable opportunities) for grasping. Separate computational models have been developed for infant grasp learning and affordance learning, but no single model has yet combined these processes in a neurobiologically plausible way. We present the Integrated Learning of Grasps and Affordances (ILGA) model that simultaneously learns grasp affordances from visual object features and motor parameters for planning grasps using trial-and-error reinforcement learning. As in the Infant Learning to Grasp Model, we model a stage of infant development prior to the onset of sophisticated visual processing of hand-object relations, but we assume that certain premotor neurons activate neural populations in primary motor cortex that synergistically control different combinations of fingers. The ILGA model is able to extract affordance representations from visual object features, learn motor parameters for generating stable grasps, and generalize its learned representations to novel objects.