用虚拟现实训练眼动预测头动，实现个性化助颈外骨骼控制。
A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons
- 通过VR采集眼动与头动数据，训练基于眼动预测头动的模型。
- 多层框架筛选控制器，两个新模型在实物外骨骼上表现优异。
- 强调个性化控制必要性，适合神经康复与智能辅具研究者。
头部下垂综合征由神经系统疾病导致的颈部肌肉无力引起，严重损害个体支撑和移动头部的能力，造成疼痛并使日常任务困难。我们的长期目标是开发一种可助力的颈部外骨骼，恢复自然运动。然而，预测用户意图的头部运动仍是关键挑战。我们利用虚拟现实（VR）从健康人群中收集眼动与头动的耦合数据，训练仅依赖眼动预测头动的模型。我们还提出一种新颖的多层控制器选择框架，将头控策略在抽象层次递减的环境中评估——从仿真、VR到物理颈部外骨骼。该流程能有效早期剔除表现不佳的控制器，识别出两个新型眼动驱动模型，在物理外骨骼部署中表现良好。结果表明，没有单一控制器适用于所有用户，凸显了眼动驱动辅助控制中个性化的重要性。本工作展示了基于VR评估在加速开发直观、安全、个性化辅助机器人方面的价值。
原文摘要 · Abstract (English)
Dropped head syndrome, caused by neck muscle weakness from neurological diseases, severely impairs an individual's ability to support and move their head, causing pain and making everyday tasks challenging. Our long-term goal is to develop an assistive powered neck exoskeleton that restores natural movement. However, predicting a user's intended head movement remains a key challenge. We leverage virtual reality (VR) to collect coupled eye and head movement data from healthy individuals to train models capable of predicting head movement based solely on eye gaze. We also propose a novel multi-layer controller selection framework, where head control strategies are evaluated across decreasing levels of abstraction -- from simulation and VR to a physical neck exoskeleton. This pipeline effectively rejects poor-performing controllers early, identifying two novel gaze-driven models that achieve strong performance when deployed on the physical exoskeleton. Our results reveal that no single controller is universally preferred, highlighting the necessity for personalization in gaze-driven assistive control. Our work demonstrates the utility of VR-based evaluation for accelerating the development of intuitive, safe, and personalized assistive robots.
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