In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors
The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/11/4/76 |
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| author | Honoria Ocagli Corrado Lanera Carlotta Borghini Noor Muhammad Khan Alessandra Casamento Dario Gregori |
| author_facet | Honoria Ocagli Corrado Lanera Carlotta Borghini Noor Muhammad Khan Alessandra Casamento Dario Gregori |
| author_sort | Honoria Ocagli |
| collection | DOAJ |
| description | The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations. |
| format | Article |
| id | doaj-art-2ef72e91651f4b0cac94c49a90e4b267 |
| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-2ef72e91651f4b0cac94c49a90e4b2672024-12-27T14:30:37ZengMDPI AGInformatics2227-97092024-10-011147610.3390/informatics11040076In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed SensorsHonoria Ocagli0Corrado Lanera1Carlotta Borghini2Noor Muhammad Khan3Alessandra Casamento4Dario Gregori5Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan, 18, 35121 Padova, ItalyThe growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations.https://www.mdpi.com/2227-9709/11/4/76artificial intelligencemachine learningpredictionin-bedmonitoringsystematic review |
| spellingShingle | Honoria Ocagli Corrado Lanera Carlotta Borghini Noor Muhammad Khan Alessandra Casamento Dario Gregori In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors Informatics artificial intelligence machine learning prediction in-bed monitoring systematic review |
| title | In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors |
| title_full | In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors |
| title_fullStr | In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors |
| title_full_unstemmed | In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors |
| title_short | In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors |
| title_sort | in bed monitoring a systematic review of the evaluation of in bed movements through bed sensors |
| topic | artificial intelligence machine learning prediction in-bed monitoring systematic review |
| url | https://www.mdpi.com/2227-9709/11/4/76 |
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