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.

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