基于生成模型的可自适应康复外骨骼，为中风患者量身定制训练轨迹。
Upper-Limb Rehabilitation with a Dual-Mode Individualized Exoskeleton Robot: A Generative-Model-Based Solution
- 用生成模型动态生成个性化运动轨迹，实时调整力控策略。
- 在健康人与中风患者实验中验证，能有效适配不同康复阶段需求。
- 支持主动模仿与被动跟随双模式，覆盖不同肌力水平患者。
针对中风后上肢康复，现有外骨骼机器人因个体化辅助能力不足而效果受限。本文提出一种新型上肢外骨骼机器人，具备在线生成能力，可根据患者实时反馈（如身体状况、反应及舒适度）动态定制精细运动轨迹。该生成机制集成于两种工作模式：适用于单侧保留运动功能患者的主动镜像模式，以及适用于双侧无运动能力患者的被动跟随模式。通过健康受试者与中风患者实验验证了其有效性，结果表明该系统能实现精准、个性化的康复训练支持。
原文摘要 · Abstract (English)
Several upper-limb exoskeleton robots have been developed for stroke rehabilitation, but their rather low level of individualized assistance typically limits their effectiveness and practicability. Individualized assistance involves an upper-limb exoskeleton robot continuously assessing feedback from a stroke patient and then meticulously adjusting interaction forces to suit specific conditions and online changes. This paper describes the development of a new upper-limb exoskeleton robot with a novel online generative capability that allows it to provide individualized assistance to support the rehabilitation training of stroke patients. Specifically, the upper-limb exoskeleton robot exploits generative models to customize the fine and fit trajectory for the patient, as medical conditions, responses, and comfort feedback during training generally differ between patients. This generative capability is integrated into the two working modes of the upper-limb exoskeleton robot: an active mirroring mode for patients who retain motor abilities on one side of the body and a passive following mode for patients who lack motor ability on both sides of the body. The performance of the upper-limb exoskeleton robot was illustrated in experiments involving healthy subjects and stroke patients.
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