为中风患者设计可感知滑动的柔性机械手，自动调节握力。
Soft Vision-Based Tactile-Enabled SixthFinger: Advancing Daily Objects Manipulation for Stroke Survivors
- 用视觉+触觉融合感知滑动，实时调整抓握力度。
- 在多种日常物品上实现稳定抓取，成功率高。
- 适合中风康复辅助，提升生活自理能力。
中风后抓握功能障碍凸显了发展先进补偿策略的迫切需求。本文提出一种新型软体、基于视觉与触觉的额外机械手指系统，帮助慢性中风患者。该系统通过视觉触觉感知检测滑动，自主调节握力，不仅保障机械稳定性，还增强触觉反馈，模拟人机交互动态。核心采用基于变压器的框架，在涵盖形状、尺寸、重量、纹理和硬度等多样属性物体的综合触觉数据集上训练。实测验证了系统在真实场景中对多种日常物品的成功操控能力。结果表明该方法具有显著潜力，可显著改善中风患者的生存质量。
原文摘要 · Abstract (English)
The presence of post-stroke grasping deficiencies highlights the critical need for the development and implementation of advanced compensatory strategies. This paper introduces a novel system to aid chronic stroke survivors through the development of a soft, vision-based, tactile-enabled extra robotic finger. By incorporating vision-based tactile sensing, the system autonomously adjusts grip force in response to slippage detection. This synergy not only ensures mechanical stability but also enriches tactile feedback, mimicking the dynamics of human-object interactions. At the core of our approach is a transformer-based framework trained on a comprehensive tactile dataset encompassing objects with a wide range of morphological properties, including variations in shape, size, weight, texture, and hardness. Furthermore, we validated the system's robustness in real-world applications, where it successfully manipulated various everyday objects. The promising results highlight the potential of this approach to improve the quality of life for stroke survivors.
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