Reinforcement Learning-Based Adaptive Biofeedback Engine for Overground Walking Speed Training
Reinforcement Learning-Based Adaptive Biofeedback Engine for Overground Walking Speed Training
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DOI：
10.1109/lra.2022.3187616
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发表时间：
2022-07-01
影响因子：
5.2
通讯作者：
Zanotto, Damiano
中科院分区：
文献类型：
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作者：
Zhang, Huanghe;Li, Shuai;Zanotto, Damiano
可穿戴生物反馈系统(WBS)已被提出用来帮助运动障碍患者的身体康复。由于个体间和个体内的显著差异，随着患者功能恢复的进展，给定的生物反馈策略对于不同的用户和治疗过程的有效性可能会有所不同。到目前为止，只有很少的研究调查了生物反馈策略的使用，这种策略可以根据用户的反应进行自我适应。本文介绍了一种新的基于模糊逻辑生物反馈引擎的强化学习方法(RLFLE)，用于个性化地面步行速度训练。该方法利用强化学习和模糊推理策略来持续调整脚下振动触觉刺激，以鼓励用户达到目标行走速度。该刺激策略还能够确定用户在地面步态训练期间的最大稳态行走速度。RLFLE是在定制的WBS中实施的，并在健康成年人的步行测试中与两种更简单的生物反馈策略进行了验证。当使用RLFLE进行训练时，参与者表现出较低的步行速度错误。此外，结果表明，新的方法在确定个人的最大稳态步行速度方面更有效。鉴于步行速度作为健康状况指标和基于运动的干预措施的基本结果的重要性，这些结果显示出在未来技术增强的步态康复方案中实施的希望。
Wearable biofeedback systems (WBS) have been proposed to aid physical rehabilitation of individuals with motor impairments. Due to significant inter- and intra-individual differences, the effectiveness of a given biofeedback strategy may vary for different users and across therapeutic sessions, as a patient's functional recovery progresses. To date, only a paucity of research has investigated the use of biofeedback strategies that can self-adapt based on the user's response. This letter introduces a novel reinforcement learning with fuzzy logic biofeedback engine (RLFLE) for personalized overground walking speed training. The method leverages reinforcement learning and a fuzzy inference strategy to continuously modulate underfoot vibrotactile stimuli that encourage users to achieve a target walking speed. This stimulation strategy also enables the determination of a user's maximum steady-state walking speed during a gait training session overground. The RLFLE was implemented in a custom-engineered WBS and validated against two simpler biofeedback strategies during walking tests with healthy adults. Participants showed lower walking speed errors when training with the RLFLE. Additionally, results indicate that the new method is more effective in determining an individual's maximum steady-state walking speed. Given the importance of walking speed as an indicator of health status and as an essential outcome of exercise-based interventions, these results show promise for implementation in future technology-enhanced gait rehabilitation protocols.