2023
Zhinin-Vera, L.; Moya, A.; Navarro, E.; Jaen, J.; Machado, J.
A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients Proceedings Article
Em: J., Urzaiz G. Bravo (Ed.): pp. 15-26, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769).
Resumo | Links | BibTeX | Etiquetas: Acquired brain injuries; Condition; Deep q-network; Gesture interaction; Multi-modal; Multimodal gesture interaction; Rehabilitation activities; Reinforcement learning algorithms; Reinforcement learnings; Telerehabilitation, Brain; Deep learning; Learning algorithms; Patient rehabilitation, Reinforcement learning
@inproceedings{Zhinin-Vera202315,
title = {A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients},
author = {L. Zhinin-Vera and A. Moya and E. Navarro and J. Jaen and J. Machado},
editor = {Urzaiz G. Bravo J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178594918&doi=10.1007%2f978-3-031-48306-6_2&partnerID=40&md5=3823104564976273c57dde7b795187c6},
doi = {10.1007/978-3-031-48306-6_2},
issn = {23673370},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {835 LNNS},
pages = {15-26},
publisher = {Springer Science and Business Media Deutschland GmbH},
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},
keywords = {Acquired brain injuries; Condition; Deep q-network; Gesture interaction; Multi-modal; Multimodal gesture interaction; Rehabilitation activities; Reinforcement learning algorithms; Reinforcement learnings; Telerehabilitation, Brain; Deep learning; Learning algorithms; Patient rehabilitation, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.