神经运动控制可穿戴增强设备:当前研究与新兴趋势。

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-10-31 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1443010
Haneen Alsuradi, Joseph Hong, Helin Mazi, Mohamad Eid
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引用次数: 0

摘要

为运动和操纵而设计的可穿戴增强装置(WA),如外骨骼和编外机器人肢体,可用于增强健康人的体能,替代或恢复受损人失去的功能。非侵入性神经运动(NM)技术,包括脑电图(EEG)和超声肌电图(sEMG),有望在大脑和WA之间实现直接而直观的交流。在介绍了历史视角之后,本综述提出了一个由 NM 控制的 WA 概念模型,分析了关键的设计方面,如硬件设计、安装方法、控制范例和感觉反馈,这些方面对用户体验有直接影响,从长远来看,对 WA 的体现有直接影响。文献概览分为三个主要领域:手部 WA、上半身 WA 和下半身 WA。综述最后强调了由 NM 控制的 WA 的主要发现、挑战和趋势。本综述激励研究人员和从业人员进一步探索和评估 WAs 的发展,以确保更好的生活质量。
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Neuro-motor controlled wearable augmentations: current research and emerging trends.

Wearable augmentations (WAs) designed for movement and manipulation, such as exoskeletons and supernumerary robotic limbs, are used to enhance the physical abilities of healthy individuals and substitute or restore lost functionality for impaired individuals. Non-invasive neuro-motor (NM) technologies, including electroencephalography (EEG) and sufrace electromyography (sEMG), promise direct and intuitive communication between the brain and the WA. After presenting a historical perspective, this review proposes a conceptual model for NM-controlled WAs, analyzes key design aspects, such as hardware design, mounting methods, control paradigms, and sensory feedback, that have direct implications on the user experience, and in the long term, on the embodiment of WAs. The literature is surveyed and categorized into three main areas: hand WAs, upper body WAs, and lower body WAs. The review concludes by highlighting the primary findings, challenges, and trends in NM-controlled WAs. This review motivates researchers and practitioners to further explore and evaluate the development of WAs, ensuring a better quality of life.

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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
Vahagn: VisuAl Haptic Attention Gate Net for slip detection. A multimodal educational robots driven via dynamic attention. LS-VIT: Vision Transformer for action recognition based on long and short-term temporal difference. Neuro-motor controlled wearable augmentations: current research and emerging trends. Editorial: Assistive and service robots for health and home applications (RH3 - Robot Helpers in Health and Home).
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