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实践操作手册:2024年瑞士苏黎世“赛博人athlon”竞赛取得成功的策略与方法

HANDSON Hand: Strategies and Approaches for Competitive Success at CYBATHLON 2024.

作者信息

Hu Xuhui, Guo Fengkai, Wei Zhikai, Chen Dapeng, Dai Junfa, Li Anran, Zhang Senhao, Orban Mostafa, Tong Yao, Hu Cong, Xu Baoguo, Zeng Hong, Song Aiguo, Guo Kai, Yang Hongbo

机构信息

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

出版信息

Bioengineering (Basel). 2025 Feb 24;12(3):228. doi: 10.3390/bioengineering12030228.

DOI:10.3390/bioengineering12030228
PMID:40150692
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11939478/
Abstract

A significant number of people with disabilities rely on assistive devices, yet these technologies often face limitations, including restricted functionality, inadequate user-centered design, and a lack of standardized evaluation metrics. While upper-limb prosthetics remain a key research focus, existing commercial solutions still fall short of meeting daily reliability and usability needs, leading to high abandonment rates. CYBATHLON integrates assistive technologies into daily living tasks, driving innovation and prioritizing user needs. In CYBATHLON 2024, the HANDSON hand secured first place in the arm prosthesis race, showcasing breakthroughs in human-robot integration. This paper presents the HANDSON hand's design, core technologies, training strategies, and competition performance, offering insights for advancing multifunctional prosthetic hands to tackle real-world challenges.

摘要

相当数量的残疾人依赖辅助设备,但这些技术往往面临局限,包括功能受限、以用户为中心的设计不足以及缺乏标准化评估指标。虽然上肢假肢仍然是关键研究重点,但现有的商业解决方案仍无法满足日常可靠性和可用性需求,导致遗弃率很高。赛博铁人三项赛将辅助技术融入日常生活任务中,推动创新并优先考虑用户需求。在2024年赛博铁人三项赛中,汉德森手在手臂假肢比赛中获得第一名,展示了人机集成方面的突破。本文介绍了汉德森手的设计、核心技术、训练策略和比赛表现,为推进多功能假手以应对现实世界挑战提供见解。

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本文引用的文献

1
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J Neuroeng Rehabil. 2022 Apr 2;19(1):38. doi: 10.1186/s12984-022-01015-5.
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Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms.假体体现:定义、测量和实验范式的系统评价。
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Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning-A Survey in Myoelectric Control.基于机器学习和深度学习的稳健、自适应且可靠的上肢运动估计——肌电控制综述
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Getting a Grip on the Impact of Incidental Feedback From Body-Powered and Myoelectric Prostheses.把握由身体驱动和肌电假肢产生的偶然反馈的影响。
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