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四肢瘫痪患者手势的可变性分析。

Variability Analysis on Gestures for People With Quadriplegia.

出版信息

IEEE Trans Cybern. 2018 Jan;48(1):346-356. doi: 10.1109/TCYB.2016.2635481. Epub 2016 Dec 26.

Abstract

Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.

摘要

基于手势的界面已成为人机交互领域中的一种有效控制方式,可帮助行动不便的人访问日常生活和娱乐所需的技术。最近的研究表明,基于手势的界面与游戏机相结合,正在康复医院和家庭中用于补充物理治疗。由于身体残疾者的运动方式与正常人不同,因此必须定制操作这些界面所需的手势集。这极大地限制了运动障碍用户可用的软件环境的数量和质量。之前的工作提出了一种分析方法,可以将为正常人设计的现有基于手势的界面转换为可由运动障碍者使用的界面。本文的目的是使用机器人作为附加验证框架,将手势可变性分析纳入现有框架中。在此基础上,获得了一种物理度量(称为功),以比较每个手势的体力消耗。提出了一种集成方法,根据稳定性和机器人的实际执行情况来确定可访问的手势集。对于所有手势类型,都发现可访问的手势位于稳定性和功的最优性的 34%以内。最后,通过实验让目标用户解决空间导航问题,实际评估了所提出方法确定的手势集。

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