Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson's patients.

Abstract

Introduction: Neurological tremors, prevalent among a large population, are one of the most rampant movement disorders. Biomechanical loading and exoskeletons show promise in enhancing patient well-being, but traditional control algorithms limit their efficacy in dynamic movements and personalized interventions. Furthermore, a pressing need exists for more comprehensive and robust validation methods to ensure the effectiveness and generalizability of proposed solutions.

Methods: This paper proposes a physical simulation approach modeling multiple arm joints and tremor propagation. This study also introduces a novel adaptable reinforcement learning environment tailored for disorders with tremors. We present a deep reinforcement learning-based encoder-actor controller for Parkinson's tremors in various shoulder and elbow joint axes displayed in dynamic movements.

Results: Our findings suggest that such a control strategy offers a viable solution for tremor suppression in real-world scenarios.

Discussion: By overcoming the limitations of traditional control algorithms, this work takes a new step in adapting biomechanical loading into the everyday life of patients. This work also opens avenues for more adaptive and personalized interventions in managing movement disorders.