Artificial intelligence technologies are considered crucial in supporting a decentralizedmodel of care in which therapeutic interventions are provided from a distance. In the last years, variousapproaches have been proposed to support remote monitoring and smart assistance in rehabilitation services.A comprehensive state-of-the-art of machine learning methods and applications is presented in this review.Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEEXplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervisedand unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologieshave been identified as strategic applications to support remote and smart monitoring. The most addressedtasks by algorithms were activity recognition, movement classification, and clinical status prediction. Someauthors evidenced drawbacks concerning the low generalizability of the results retrieved.Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitationservices by providing broad access to sustained and high-quality therapy. Future efforts are needed tovalidate artificial intelligence technologies in specific clinical populations and evaluate results reliabilityin remote conditions and home-based settings.
The Role of Artificial Intelligence in Future Rehabilitation Services: a Systematic Literature Review
MennellaPrimo
;Maniscalco
Secondo
;De PietroPenultimo
;MassimoUltimo
2023
Abstract
Artificial intelligence technologies are considered crucial in supporting a decentralizedmodel of care in which therapeutic interventions are provided from a distance. In the last years, variousapproaches have been proposed to support remote monitoring and smart assistance in rehabilitation services.A comprehensive state-of-the-art of machine learning methods and applications is presented in this review.Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEEXplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervisedand unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologieshave been identified as strategic applications to support remote and smart monitoring. The most addressedtasks by algorithms were activity recognition, movement classification, and clinical status prediction. Someauthors evidenced drawbacks concerning the low generalizability of the results retrieved.Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitationservices by providing broad access to sustained and high-quality therapy. Future efforts are needed tovalidate artificial intelligence technologies in specific clinical populations and evaluate results reliabilityin remote conditions and home-based settings.File | Dimensione | Formato | |
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