下肢外骨骼能否支持没有拐杖的体弱老人从坐到站的运动?优化控制与运动捕捉相结合的研究

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jan C. L. Lau, Katja Mombaur
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引用次数: 0

摘要

预计到 2050 年,全球老年人口将达到 15 亿,因此,人们开发了不同的辅助技术来解决与年龄相关的运动障碍问题。下肢机器人外骨骼有可能在促进日常生活活动的同时为体弱的老年人提供支持,但对这一人群来说,拐杖的需求可能具有挑战性。拐杖有助于提高安全性和稳定性,但在外骨骼中移动时,拐杖可能会使人的动作不自然,协调起来也很困难。体弱的老年人可能没有足够的臂力使用拐杖,或者长期使用会导致上肢关节退化。本文介绍的研究有助于对无拐杖外骨骼的使用进行更详细的研究,尤其是分析最具挑战性的动作--从坐到站(STS)。它结合了运动捕捉和优化控制方法,评估并比较了有拐杖和无拐杖 TWIN 外骨骼的 STS 动态效果。结果显示,其轨迹明显快于外骨骼的默认轨迹,并确定了完全和部分 STS 辅助所需的电机扭矩。TWIN 外骨骼现有电机的总扭矩为 112 牛米(髋关节)和 88 牛米(膝关节),假设设备和用户都能提供理想的扭矩,则老年人需要提供 8 牛米(髋关节)和 50 牛米(膝关节)的扭矩。若要让 TWIN 提供全面的 STS 辅助功能,则需要新的电机,其总输出功率至少为 121 牛米(髋关节)和 140 牛米(膝关节)。所介绍的优化控制方法可在其他外骨骼上复制,以确定其质量分布所需的扭矩。会上还讨论了未来的改进措施,所展示的结果为在使用外骨骼移动时取消拐杖奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can lower-limb exoskeletons support sit-to-stand motions in frail elderly without crutches? A study combining optimal control and motion capture
With the global geriatric population expected to reach 1.5 billion by 2050, different assistive technologies have been developed to tackle age-associated movement impairments. Lower-limb robotic exoskeletons have the potential to support frail older adults while promoting activities of daily living, but the need for crutches may be challenging for this population. Crutches aid safety and stability, but moving in an exoskeleton with them can be unnatural to human movements, and coordination can be difficult. Frail older adults may not have the sufficient arm strength to use them, or prolonged usage can lead to upper limb joint deterioration. The research presented in this paper makes a contribution to a more detailed study of crutch-less exoskeleton use, analyzing in particular the most challenging motion, sit-to-stand (STS). It combines motion capture and optimal control approaches to evaluate and compare the STS dynamics with the TWIN exoskeleton with and without crutches. The results show trajectories that are significantly faster than the exoskeleton's default trajectory, and identify the motor torques needed for full and partial STS assistance. With the TWIN exoskeleton's existing motors being able to support 112 Nm (hips) and 88 Nm (knees) total, assuming an ideal contribution from the device and user, the older adult would need to contribute a total of 8 Nm (hips) and 50 Nm (knees). For TWIN to provide full STS assistance, it would require new motors that can exert at least 121 Nm (hips) and 140 Nm (knees) total. The presented optimal control approaches can be replicated on other exoskeletons to determine the torques required with their mass distributions. Future improvements are discussed and the results presented lay groundwork for eliminating crutches when moving with an exoskeleton.
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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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