Reward can increase the speed and accuracy of movements in both simple and sequential reaching tasks. Two mechanisms are thought to be responsible for this: an increase in maximum velocity, due to increased muscle stiffness, resulting in faster, but energetically inefficient, individual movements; or coarticulation; the blending of sub-movements into single, smoother, more energetically efficient movements. Older adults have shown reduced sensitivity to reward in decision paradigms, but there is little research relating reward and motor performance in older adults. Using a novel online sequential reaching task, we compared the effects of reward on motor performance between young (18-23 years) and older (65-79 years) participants. We found that movement time decreased across training in all groups, and reward invigorated this decrease in both age groups. This suggests that reward is a viable facilitator of motor performance to compensate for age-related motor decline and has the potential for use in the design of rehabilitation programmes for age-related motor deficits or disease.
Optical marker-less hand-tracking systems incorporated into virtual reality (VR) headsets are transforming the ability to assess motor skills, including hand movements, in VR. This promises to have far-reaching implications for the increased applicability of VR across scientific, industrial and clinical settings. However, so far, there is little data regarding the accuracy, delay and overall performance of these types of hand-tracking systems. Here we present a novel methodological framework which can be easily applied to measure these systems' absolute positional error, temporal delay and finger joint-angle accuracy. We used this framework to evaluate the Meta Quest 2 hand-tracking system. Our results showed an average fingertip positional error of 1.1 cm, an average finger joint angle error of 9.6° and an average temporal delay of 38.0 ms. Finally, a novel approach was developed to correct for these positional errors based on a lens distortion model. This methodological framework provides a powerful tool to ensure the reliability and validity of data originating from VR-based, marker-less hand-tracking systems.
Optical markerless hand-tracking systems incorporated into virtual reality (VR) headsets are transforming the ability to assess fine motor skills in VR. This promises to have far-reaching implications for the increased applicability of VR across scientific, industrial, and clinical settings. However, so far, there are little data regarding the accuracy, delay, and overall performance of these types of hand-tracking systems. Here we present a novel methodological framework based on a fixed grid of targets, which can be easily applied to measure these systems’ absolute positional error and delay. We also demonstrate a method to assess finger joint-angle accuracy. We used this framework to evaluate the Meta Quest 2 hand-tracking system. Our results showed an average fingertip positional error of 1.1cm, an average finger joint angle error of 9.6∘ and an average temporal delay of 45.0 ms. This methodological framework provides a powerful tool to ensure the reliability and validity of data originating from VR-based, markerless hand-tracking systems.
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