发表机构
University of Utah; Rocky Mountain Center for Occupational and Environmental Health, University of Utah（犹他大学; 落基山职业与环境健康中心，犹他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于前庭眼反射的眼动追踪控制器，用于动力颈部外骨骼，通过个性化参数实现自然头颈运动，相比先前模型提升24%的意图检测准确率。
AI 中文摘要
动力颈部外骨骼被开发用于恢复颈部肌肉无力患者的头颈运动。当前使用手持输入设备控制这些机器人系统的方法对这些人群而言不直观或不可行。相反，眼睛与头部协调完成许多日常任务。因此，眼睛的运动可以成为控制颈部外骨骼的有用信号，无需使用双手。然而，先前的控制模型缺乏生物行为学基础以及个性化控制行为的手段，导致不自然的头颈运动。本文提出了一种新颖的眼动追踪控制器设计，利用前庭眼反射来模拟自然的眼-头协调。还设计了个性化工具，包括滑块，允许用户调整该控制器的参数。在此，我们展示了该系统在人类用户中的技术设计和验证。与先前的眼动追踪模型相比，新控制器通过更自然的运动速度曲线和平均24%的准确运动意图检测提升，提供了为用户个性化的自然头部运动。该系统为在个性化和偏好背景下研究眼动追踪控制器提供了强大平台，并最终恢复患者群体失去的头颈活动能力。
英文摘要
Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices to control these robotic systems are unintuitive or non-viable for these populations. Alternatively, the eyes coordinate with the head to accomplish many daily tasks. Therefore, movements of the eyes can be a useful signal to control the neck exoskeletons without needing to use the hands. However, previous control models lack biobehavioral basis as well as means to personalize the control behavior, causing unnatural head-neck movements. This paper presents a novel eye-tracking controller design leveraging the vestibulo-ocular reflex to emulate natural eyehead coordination. Personalization tools were also designed, consisting of sliders to allow users to adjust the parameters of this controller. Here, we present the technical design and validation of this system in human users. Compared to the previous eye-tracking model, the new controller provides natural head motions personalized to the user through more natural velocity profiles and a 24 percent average increase of accurate motion intent detection. This system provides a powerful platform for studying eye-tracking controllers in the context of personalization and preference, and ultimately restoring the lost head-neck mobility in patient populations.
