TY - GEN
T1 - Reinforcement Learning-Based Adaptive Biofeedback Engine for Overground Walking Speed Training
AU - Zhang, Huanghe
AU - Li, Shuai
AU - Zhao, Qingya
AU - Rao, Ashwini K.
AU - Guo, Yi
AU - Zanotto, Damiano
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Wearable biofeedback system
KW - fuzzy logic
KW - gait training
KW - human-in-The-loop
KW - instrumented footwear
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85141826407&partnerID=8YFLogxK
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U2 - 10.1109/BioRob52689.2022.9925339
DO - 10.1109/BioRob52689.2022.9925339
M3 - Conference contribution
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
BT - BioRob 2022 - 9th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics
PB - IEEE Computer Society
T2 - 9th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2022
Y2 - 21 August 2022 through 24 August 2022
ER -