Learning to Suppress Tremors: A Deep Reinforcement Learning-Enabled Soft Exoskeleton for Parkinson's Patients

Tamás Endrei ^1,2 Sándor Földi ^1,2 Ádám Makk ³ György Cserey ^1,2*

• ¹ Pázmány Péter Catholic University, Budapest, Hungary
• ² Jedlik Innovation Ltd, Budapest, Hungary
• ³ Andras Peto Faculty, Semmelweis University, Budapest, Hungary

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, traditional control algorithms limit their efficacy in dynamic movements and personalized interventions. Furthermore, there exists a pressing need for more comprehensive and robust validation methods to ensure the effectiveness and generalizability of proposed solutions. 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. Furthermore we present a deep reinforcement learning based encoder-actor controller for Parkinson's tremor present in various shoulder and elbow joint axes, displayed in dynamic movements. Our findings suggest that a deep reinforcement learning based control strategy offers a viable solution for tremor suppression in real-world scenarios. By overcoming the limitations of traditional control algorithms, this work takes a new step in the adaptation of biomechanical loading into the everyday life of patients. This work also opens avenues for more adaptive and personalized interventions in managing movement disorders.