Multi-sensory fusion of wearable sensors for automatic grasping and releasing with soft-hand exoskeleton

Guan Erjiage, John Nassour, Gordon Cheng
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Abstract

This paper presents a fully integrated soft-hand exoskeleton enabling automated grasping and releasing functions thanks to the multi-sensory fusion. We use enfolded soft textile actuators to assist the hand, IMU sensors for the arm and hand orientations, and customized soft sensors for tactile feedback from the fingers. We propose a state machine controller that uses the information from tactile sensors and the IMUs to switch between different states to trigger grasping and releasing. The control strategy requires no additional user input; it is designed for meal-eating scenarios. Ten healthy participants instructed not to move their hands tested the system performing 190 trials on five tasks: pouring, drinking, eating a fruit, using a fork, and using a spoon. Objects are randomly placed in four different locations in front of the participant. 97.4% of the trials were successfully accomplished. Furthermore, 78.1% grasps and 83.8% releases are triggered by the first attempt. Compared with no assistant condition of a healthy hand, the system reduced 32.2% of muscle activities and required 2.57 more times to finish the task.
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软手外骨骼自动抓放可穿戴传感器的多感官融合
本文介绍了一种完全集成的软手外骨骼,通过多感官融合实现自动抓取和释放功能。我们使用包裹的柔软纺织品致动器来辅助手,IMU传感器用于手臂和手的方向,定制的软传感器用于手指的触觉反馈。我们提出了一种状态机控制器,该控制器利用来自触觉传感器和imu的信息在不同状态之间切换以触发抓取和释放。控制策略不需要额外的用户输入;它是为吃饭场景设计的。10名健康的参与者被指示不要移动他们的手,测试了该系统,在5项任务中进行了190次试验:倒酒、喝水、吃水果、用叉子和用勺子。物体被随机放置在参与者面前的四个不同位置。97.4%的试验完成率。此外,78.1%的抓取和83.8%的释放是由首次尝试触发的。与没有健康手的辅助条件相比,该系统减少了32.2%的肌肉活动,完成任务所需的时间增加了2.57次。
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