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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Bibliographic Details
Published in:Informatics (Basel) 2024-10, Vol.11 (4), p.76
Main Authors: Ocagli, Honoria, Lanera, Corrado, Borghini, Carlotta, Khan, Noor Muhammad, Casamento, Alessandra, Gregori, Dario
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Bed movements
Deep learning
Heterogeneity
Hospitals
in-bed
Machine learning
monitoring
Nursing homes
Older people
Patients
prediction
Pressure distribution
Pressure ulcers
Prevention
Remote monitoring
Sensors
Sleep disorders
Smart sensors
Systematic review
Wearable computers
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Summary: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.
ISSN:2227-9709
2227-9709
DOI:10.3390/informatics11040076