Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients
IntroductionNeurological 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 int...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2025.1537470/full |
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| author | Tamás Endrei Tamás Endrei Sándor Földi Sándor Földi Ádám Makk György Cserey György Cserey |
| author_facet | Tamás Endrei Tamás Endrei Sándor Földi Sándor Földi Ádám Makk György Cserey György Cserey |
| author_sort | Tamás Endrei |
| collection | DOAJ |
| description | IntroductionNeurological 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.MethodsThis 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.ResultsOur findings suggest that such a control strategy offers a viable solution for tremor suppression in real-world scenarios.DiscussionBy 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. |
| format | Article |
| id | doaj-art-c2f0f361a6504c4d8b973d3ee1fa047b |
| institution | OA Journals |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-c2f0f361a6504c4d8b973d3ee1fa047b2025-08-20T02:33:14ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-05-011210.3389/frobt.2025.15374701537470Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patientsTamás Endrei0Tamás Endrei1Sándor Földi2Sándor Földi3Ádám Makk4György Cserey5György Cserey6Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, HungaryJedlik Innovation Ltd., Budapest, HungaryFaculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, HungaryJedlik Innovation Ltd., Budapest, HungaryAndrás Pető Faculty, Semmelweis University, Budapest, HungaryFaculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, HungaryJedlik Innovation Ltd., Budapest, HungaryIntroductionNeurological 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.MethodsThis 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.ResultsOur findings suggest that such a control strategy offers a viable solution for tremor suppression in real-world scenarios.DiscussionBy 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.https://www.frontiersin.org/articles/10.3389/frobt.2025.1537470/fulldeep reinforcement learningsoft exoskeletonParkinson’s diseasetremorphysics simulationhuman–robot interaction |
| spellingShingle | Tamás Endrei Tamás Endrei Sándor Földi Sándor Földi Ádám Makk György Cserey György Cserey Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients Frontiers in Robotics and AI deep reinforcement learning soft exoskeleton Parkinson’s disease tremor physics simulation human–robot interaction |
| title | Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients |
| title_full | Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients |
| title_fullStr | Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients |
| title_full_unstemmed | Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients |
| title_short | Learning to suppress tremors: a deep reinforcement learning-enabled soft exoskeleton for Parkinson’s patients |
| title_sort | learning to suppress tremors a deep reinforcement learning enabled soft exoskeleton for parkinson s patients |
| topic | deep reinforcement learning soft exoskeleton Parkinson’s disease tremor physics simulation human–robot interaction |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2025.1537470/full |
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