Abstract
Acquired Brain Injury (ABI) is a condition caused by an injury or disease that disrupts the normal functioning of the brain. In recent years, there has been a significant increase in the incidence of ABI, highlighting the need for a comprehensive approach that improves the rehabilitation process and, thus, provides people with ABI with a better quality of life. Developing appropriate rehabilitation activities for these patients is a major challenge for experts in the field, as their poor design can hinder the recovery process. One way to address this problem is through the use of smart systems that generate such rehabilitation activities in an automatic way that can then be modified by therapists as they deem appropriate. This automatic generation of rehabilitation activities uses experts’ knowledge to determine their suitability according to the patient’s needs. The problem is that this knowledge may be ill-defined, hampering the rehabilitation process. This paper investigates the possibility of applying Deep Q-Networks, a Reinforcement Learning (RL) algorithm, to evolve and adapt that information according to the outcomes of the rehabilitation process of groups of patients. This will help minimize possible errors made by experts and improve the rehabilitation process.
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Acknowledgements
This paper is part of the R+D+i projects PID2019-108915RB-I00 and PID2022-140907OB-I00, and the grant PRE2020-094056 funded by MCIN/AEI/ 10.13039/501100011033. It has also been funded by the University of Castilla-La Mancha as part of the project 2022-GRIN-34436, by ’ERDF A way to make Europe’ and thanks to the PhD scholarship 2019-PREDUCLM-10772 (co-financed by the FSE Operational Programme 2014–2020 of Castilla-La Mancha through Axis 3).
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Zhinin-Vera, L., Moya, A., Navarro, E., Jaen, J., Machado, J. (2023). A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-031-48306-6_2
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