设计和分析用于桡骨远端骨折康复的外骨骼装置。

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-10-18 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1477232
Zhiquan Chen, Jiabao Guo, Yishan Liu, Mengqian Tian, Xingsong Wang
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引用次数: 0

摘要

本研究揭示了桡骨远端骨折(DRF)腕部外固定和阻力训练的力学原理。根据生物力学分析,提出了两种可穿戴外骨骼装置,以促进桡骨远端骨折的康复进展。从时间上看,可调节固定装置(AFD)可在早期为骨折腕部提供固定保护和有限的活动能力,而阻力训练装置(RTD)则可在后期实现相关肌肉的功能恢复。根据所设计的 AFD 和 RTD 机电系统,建立了这两个装置的实验原型。通过实验,研究了 AFD 的实际运动范围,并验证了监测关节角度的可行性。同时,根据表面肌电图(sEMG)信号特征分析了 RTD 的阻力影响,结果表明训练引起的肌肉力量增强一般随外部阻力的增加而增加。本研究提出的外骨骼装置将有利于 DRF 患者的积极康复。
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Design and analysis of exoskeleton devices for rehabilitation of distal radius fracture.

In this work, the mechanical principles of external fixation and resistance training for the wrist affected by a distal radius fracture (DRF) are revealed. Based on the biomechanical analysis, two wearable exoskeleton devices are proposed to facilitate the DRF rehabilitation progress. Chronologically, the adjustable fixation device (AFD) provides fixed protection and limited mobilization of the fractured wrist in the early stage, while the functional recovery of relevant muscles is achieved by the resistance training device (RTD) in the later stage. According to the designed mechatronic systems of AFD and RTD, the experimental prototypes for these two apparatuses are established. By experiments, the actual motion ranges of AFD are investigated, and the feasibility in monitoring joint angles are validated. Meanwhile, the resistant influences of RTD are analyzed based on the surface electromyography (sEMG) signal features, the results demonstrate that the training-induced muscle strength enhancement is generally increased with the increment in external resistance. The exoskeleton devices presented in this work would be beneficial for the active rehabilitation of patients with DRF.

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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.
期刊最新文献
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