标题： 使用视觉学习用户交互力的软指外骨骼 显示英文标题

标题： Learning User Interaction Forces using Vision for a Soft Finger Exosuit

摘要： 可穿戴辅助设备正变得越来越柔软。 对其与人体组织的界面进行建模是必要的，以捕捉动态辅助的传递。 然而，其非线性和柔性的特点使得物理建模和嵌入式传感都具有挑战性。 在本文中，我们开发了一个基于图像、学习的框架，用于估计指套外骨骼系统的分布式接触力。 我们使用SoRoSim工具箱生成多样化的外骨骼几何形状和驱动场景数据集进行训练。 该方法能够从低分辨率灰度图像中准确估计多个接触位置的交互力，能够推广到未见过的形状和驱动水平，并在视觉噪声和对比度变化下保持鲁棒性。 我们将模型集成到反馈控制器中，并发现基于视觉的估计器可作为闭环控制的替代力传感器。 这种方法可以作为外骨骼实时力估计的一种非侵入性替代方案。 显示英文摘要

摘要： Wearable assistive devices are increasingly becoming softer. Modelling their interface with human tissue is necessary to capture transmission of dynamic assistance. However, their nonlinear and compliant nature makes both physical modeling and embedded sensing challenging. In this paper, we develop a image-based, learning-based framework to estimate distributed contact forces for a finger-exosuit system. We used the SoRoSim toolbox to generate a diverse dataset of exosuit geometries and actuation scenarios for training. The method accurately estimated interaction forces across multiple contact locations from low-resolution grayscale images, was able to generalize to unseen shapes and actuation levels, and remained robust under visual noise and contrast variations. We integrated the model into a feedback controller, and found that the vision-based estimator functions as a surrogate force sensor for closed-loop control. This approach could be used as a non-intrusive alternative for real-time force estimation for exosuits.