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Designing Prosthetic Hands With Embodied Intelligence: The KIT Prosthetic Hands

计算机科学 假手 欠驱动 机电一体化 人工智能 惯性测量装置 可扩展性 人机交互 任务(项目管理) 机器人学 稳健性(进化) 计算机视觉 控制(管理) 机器人 工程类 基因 化学 系统工程 数据库 生物化学
作者
Pascal Weiner,Julia Starke,Samuel Rader,Felix Hundhausen,Tamim Asfour
出处
期刊:Frontiers in Neurorobotics [Frontiers Media]
卷期号:16: 815716-815716 被引量:47
标识
DOI:10.3389/fnbot.2022.815716
摘要

Hand prostheses should provide functional replacements of lost hands. Yet current prosthetic hands often are not intuitive to control and easy to use by amputees. Commercially available prostheses are usually controlled based on EMG signals triggered by the user to perform grasping tasks. Such EMG-based control requires long training and depends heavily on the robustness of the EMG signals. Our goal is to develop prosthetic hands with semi-autonomous grasping abilities that lead to more intuitive control by the user. In this paper, we present the development of prosthetic hands that enable such abilities as first results toward this goal. The developed prostheses provide intelligent mechatronics including adaptive actuation, multi-modal sensing and on-board computing resources to enable autonomous and intuitive control. The hands are scalable in size and based on an underactuated mechanism which allows the adaptation of grasps to the shape of arbitrary objects. They integrate a multi-modal sensor system including a camera and in the newest version a distance sensor and IMU. A resource-aware embedded system for in-hand processing of sensory data and control is included in the palm of each hand. We describe the design of the new version of the hands, the female hand prosthesis with a weight of 377 g, a grasping force of 40.5 N and closing time of 0.73 s. We evaluate the mechatronics of the hand, its grasping abilities based on the YCB Gripper Assessment Protocol as well as a task-oriented protocol for assessing the hand performance in activities of daily living. Further, we exemplarily show the suitability of the multi-modal sensor system for sensory-based, semi-autonomous grasping in daily life activities. The evaluation demonstrates the merit of the hand concept, its sensor and in-hand computing systems.
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