跳到正文
原文
arXiv · Human-Computer Interaction· Andrew L. Huard, Avinash Ajit Nargund, Misha Sra·· 7 小时前

通过实时视觉反馈外化手握压力

Externalizing Hand Grip Pressure Through Real-Time Visual Feedback

AI 导读

在N=28的新手握力匹配任务中,连续渐变可视化比离散条形显示匹配更准确、注视熵更低,尽管被试认为条形更精确。

摘要

arXiv:2610.36769v1 Announce Type: new Abstract: Grip pressure, including contact force and its distribution across the hand, is central to skilled grasping but is not directly visible. This makes it difficult to teach, learn, and self-correct in grip-dependent motor tasks, where verbal instruction and tactile demonstration provide only partial guidance and no real-time, spatially resolved feedback tied to the learner's own grip. Using an instrumented glove, we compare pressure feedback visualizations for such guidance, evaluating how visualization type and, secondarily, sensor-region mapping shape pressure-matching performance in novices. In a study using baseball and table tennis as representative grip tasks (N=28), we find that a continuous gradient visualization produces significantly more accurate pressure matches and lower gaze entropy than a discrete bar display, despite participants perceiving the bar as more precise for pressure magnitude. Perceived precision and objective performance therefore diverged: the encoding participants judged more precise was not the one they matched more accurately with. Grip-tailored sensor-region mappings further reduced perceived temporal demand and effort, although this subjective workload advantage did not extend to objective matching accuracy. Overall, continuous pressure feedback supported more accurate grip matching and more concentrated viewing, and we present design considerations for real-time pressure feedback in motor skill guidance.

中文翻译还没有生成,先显示原文。

来源:arXiv · Human-Computer Interaction · arxiv.org

相关事件