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首页> 外文期刊>International Journal of Advanced Robotic Systems >A Hands-free Interface for Controlling Virtual Electric-powered Wheelchairs
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A Hands-free Interface for Controlling Virtual Electric-powered Wheelchairs

机译：用于控制虚拟电动轮椅的免提界面

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This paper focuses on how to provide mobility to people with motor impairments with the integration of robotics and wearable computing systems. The burden of learning to control powered mobility devices should not fall entirely on the people with disabilities. Instead, the system should be able to learn the user's movements. This requires learning the degrees of freedom of user movement, and mapping these degrees of freedom onto electric-powered wheelchair (EPW) controls. Such mapping cannot be static because in some cases users will eventually improve with practice. Our goal in this paper is to present a hands-free interface (HFI) that can be customized to the varying needs of EPW users with appropriate mapping between the users' degrees of freedom and EPW controls. EPW users with different impairment types must learn how to operate a wheelchair with their residual body motions. EPW interfaces are often customized to fit their needs. An HFI utilizes the signals generated by the user's voluntary shoulder and elbow movements and translates them into an EPW control scheme. We examine the correlation of kinematics that occur during moderately paced repetitive elbow and shoulder movements for a range of motion. The output of upper-limb movements (shoulder and elbows) was tested on six participants, and compared with an output of a precision position tracking (PPT) optical system for validation. We find strong correlations between the HFI signal counts and PPT optical system during different upper-limb movements (ranged from r = 0.86 to 0.94). We also tested the HFI performance in driving the EPW in a virtual reality environment on a spinal-cord-injured (SCI) patient. The results showed that the HFI was able to adapt and translate the residual mobility of the SCI patient into efficient control commands within a week's training. The results are encouraging for the development of more efficient HFIs, especially for wheelchair users.
机译：本文重点介绍如何为机器人和可穿戴计算系统的集成提供电机障碍的移动性。学习控制动力移动设备的负担不应完全落在残疾人身上。相反，系统应该能够学习用户的运动。这需要了解用户运动的自由度，并将这些自由度映射到电动轮椅（EPW）控制上。这种映射不能静态，因为在某些情况下，用户最终将随着实践而改善。本文的目标是展示一种免提界面（HFI），可以在用户自由度和EPW控件之间具有适当的映射的EPW用户的不同需求。具有不同损伤类型的EPW用户必须学习如何使用其残留的身体运动操作轮椅。 EPW接口通常是自定义的，以满足他们的需求。 HFI利用用户自愿肩部和肘部运动产生的信号，并将其转化为EPW控制方案。我们检查运动学中的运动学的相关性，其在中度节奏的重复弯头和肩部运动中进行一系列运动。上肢运动（肩部和肘部）的输出在六个参与者上进行测试，并与精密位置跟踪（PPT）光学系统的输出进行比较，用于验证。我们在不同的上肢运动期间找到了HFI信号计数和PPT光学系统之间的强烈相关性（从r = 0.86到0.94）。我们还测试了在脊髓损伤（SCI）患者的虚拟现实环境中驱动EPW的HFI性能。结果表明，HFI能够在一周的培训中适应和将SCI患者的残余流动性调整和转化为有效的控制命令。结果令人鼓舞的是开发更高效的HFI，特别是轮椅用户。

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