Elbow-sideWINDER (Elbow-side Wearable INDustrial Ergonomic Robot): design, control, and validation of a novel elbow exoskeleton.

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Daegeun Park, Christian Di Natali, Matteo Sposito, Darwin G Caldwell, Jesus Ortiz
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引用次数: 0

Abstract

Musculoskeletal Disorders associated with the elbow are one of the most common forms of work-related injuries. Exoskeletons have been proposed as an approach to reduce and ideally eliminate these injuries; however, exoskeletons introduce their own problems, especially discomfort due to joint misalignment. The Elbow-sideWINDER with its associated control strategy is a novel elbow exoskeleton to assist elbow flexion/extension during occupational tasks. This study describes the exoskeleton showing how this can minimize discomfort caused by joint misalignment, maximize assistive performance, and provide increased robustness and reliability in real worksites. The proposed medium-level control strategy can provide effective assistive torque using three control units as follows: an arm kinematics estimator, a load estimator, and a friction compensator. The combined hardware/software system of the Elbow-sideWINDER is tested in load-lifting tasks (2 and 7 kg). This experiment focuses on the reduction in the activation level of the biceps brachii and triceps brachii in both arms and the change in the range of motion of the elbow during the task. It is shown that using the Elbow-sideWINDER, the biceps brachii, responsible for the elbow flexion, was significantly less activated (up to 38.8% at 2 kg and 25.7% at 7 kg, on average for both arms). For the triceps brachii, the muscle activation was reduced by up to 37.0% at 2 kg and 35.1% at 7 kg, on average for both arms. When wearing the exoskeleton, the range of motion of the elbow was reduced by up to 13.0° during the task, but it was within a safe range and could be compensated for by other joints such as the waist or knees. There are extremely encouraging results that provide good indicators and important clues for future improvement of the Elbow-sideWINDER and its control strategy.

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肘部可穿戴工业人体工程学机器人:一种新型肘部外骨骼的设计、控制和验证。
与肘部相关的肌肉骨骼疾病是最常见的工伤形式之一。外骨骼已被提出作为减少和理想地消除这些损伤的方法;然而,外骨骼也有自己的问题,尤其是关节错位引起的不适。肘关节侧弯器及其相关控制策略是一种新型肘关节外骨骼,可在职业任务中辅助肘关节屈伸。这项研究描述了外骨骼,展示了它如何最大限度地减少关节错位引起的不适,最大限度地提高辅助性能,并在实际工作场所提供更高的稳健性和可靠性。所提出的中级控制策略可以通过以下三个控制单元提供有效的辅助扭矩:手臂运动估计器、负载估计器和摩擦补偿器。Elbow-sideWINDER的组合硬件/软件系统在起重任务(2和7公斤)中进行了测试。本实验关注的是在任务过程中,双臂肱二头肌和肱三头肌激活水平的降低以及肘关节活动范围的变化。研究表明,使用肘侧弯器时,负责肘部屈曲的肱二头肌的活动明显减少(在2公斤时达到38.8%,在7公斤时达到25.7%,双臂平均)。对于肱三头肌,在2公斤时肌肉激活减少了37.0%,在7公斤时平均减少了35.1%。当佩戴外骨骼时,肘部的活动范围在任务期间减少了13.0°,但它在安全范围内,可以通过腰部或膝盖等其他关节来补偿。研究结果非常令人鼓舞,为今后改进弯尾蛇及其控制策略提供了良好的指标和重要的线索。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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