Antunes, André2024-03-112024-03-112023-12-03979-840070921-0PURE: 83894222PURE UUID: 2b60c9c2-efb3-408e-9922-e5111d118f7eScopus: 85180128733http://hdl.handle.net/10362/164729Funding Information: This work is funded by Fundação para a Ciência e a Tecnologia (FCT) through a Ph.D. Studentship grant (2022.12093.BD). Publisher Copyright: © 2023 Owner/Author.Smart healthcare platforms continuously gather user data, allowing the application of artificial intelligence techniques for the adaptation of solutions, and providing acquired knowledge that can be shared among clinicians, therapists, and patients. Data analytics and machine learning techniques are used to extract information from the sampled data, generating knowledge so systems can learn from it. Simulation of therapeutic scenarios becomes possible, allowing inference of possible outcomes and providing tools for decision support. Therefore, this research focuses on the design of an AI-augmented digital twin towards adaptive serious-games therapy for people with disabilities (e.g., cerebral palsy, speech disorders, Parkinson's disease) to provide solutions toward positive therapy results. Finding an adequate digital twin for therapy with people with disabilities is relevant, aiming at prediction and simulation in the context of using serious games for therapy engagement.3431320engAdaptationDigital TwinDisabilitiesMachine LearningSmart EnvironmentsTherapyHuman-Computer InteractionComputer Networks and CommunicationsComputer Vision and Pattern RecognitionSoftwareconference object10.1145/3626705.3632612https://www.scopus.com/pages/publications/85180128733