The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review
Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation servic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10015010/ |
| _version_ | 1811169743823962112 |
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| author | Ciro Mennella Umberto Maniscalco Giuseppe De Pietro Massimo Esposito |
| author_facet | Ciro Mennella Umberto Maniscalco Giuseppe De Pietro Massimo Esposito |
| author_sort | Ciro Mennella |
| collection | DOAJ |
| description | Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. 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 IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings. |
| first_indexed | 2024-04-10T16:46:51Z |
| format | Article |
| id | doaj.art-b1ef526e6d4e4bb4a25280a6b1e335b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T16:46:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1ef526e6d4e4bb4a25280a6b1e335b32023-02-08T00:00:48ZengIEEEIEEE Access2169-35362023-01-0111110241104310.1109/ACCESS.2023.323608410015010The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature ReviewCiro Mennella0https://orcid.org/0000-0003-0419-7181Umberto Maniscalco1https://orcid.org/0000-0002-7157-8411Giuseppe De Pietro2Massimo Esposito3https://orcid.org/0000-0002-7196-7994Institute for High-Performance Computing and Networking (ICAR), Research National Council of Italy (CNR), Rende, ItalyInstitute for High-Performance Computing and Networking (ICAR), Research National Council of Italy (CNR), Rende, ItalyInstitute for High-Performance Computing and Networking (ICAR), Research National Council of Italy (CNR), Rende, ItalyInstitute for High-Performance Computing and Networking (ICAR), Research National Council of Italy (CNR), Rende, ItalyArtificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. 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 IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.https://ieeexplore.ieee.org/document/10015010/Digital therapeuticse-healthremote monitoringintelligent systemsdeep learningmachine learning |
| spellingShingle | Ciro Mennella Umberto Maniscalco Giuseppe De Pietro Massimo Esposito The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review IEEE Access Digital therapeutics e-health remote monitoring intelligent systems deep learning machine learning |
| title | The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review |
| title_full | The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review |
| title_fullStr | The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review |
| title_full_unstemmed | The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review |
| title_short | The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review |
| title_sort | role of artificial intelligence in future rehabilitation services a systematic literature review |
| topic | Digital therapeutics e-health remote monitoring intelligent systems deep learning machine learning |
| url | https://ieeexplore.ieee.org/document/10015010/ |
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