People with Parkinson's Disease experience gait impairments that significantly impact their quality of life. Visual, auditory, and tactile cues can alleviate gait impairments, but they can become less effective due to the progressive nature of the disease and changes in people's motor capability. In this study, we develop a human-in-the-loop (HIL) framework that monitors two key gait parameters, stride length and cadence, and continuously learns a person-specific model of how the parameters change in response to the feedback. The model is then used in an optimization algorithm to improve the gait parameters. This feasibility study examines whether auditory cues can be used to influence stride length in people without gait impairments. The results demonstrate the benefits of the HIL framework in maintaining people's stride length in the presence of a secondary task.

翻译：帕金森病患者常出现步态障碍，严重影响其生活质量。视觉、听觉与触觉提示虽能缓解步态障碍，但随着疾病进展及患者运动能力的变化，这些提示的有效性会逐渐降低。本研究提出一种人机协同（HIL）框架，该框架可监测步长和步频两个关键步态参数，并持续学习反映参数随反馈变化的个体化模型，进而通过优化算法改善步态参数。本可行性研究旨在探究听觉提示能否用于调节非步态障碍人群的步长。结果表明，该HIL框架在执行辅助任务时能有效维持受试者的步长，展现出显著优势。