AMIUGraph: Analysis and Modeling of Interactions for Utility-driven Benchmarking of Graph-Based Models in Healthcare

Journal of BMC Medical Informatics and Decision Making - -2026

Authors

Sorino Paolo, De Bellis Alessandro, Malitesta Daniele, Lofu Domenico, Bonfiglio Catia, Donghia Rossella, Giannelli Gianluigi, Ferrara Antonio, Narducci Fedelucio, Di Noia Tommaso, Di Sciascio Eugenio

Abstract

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications.

DOI

https://doi.org/10.3390/s25123816

BibTex references

@Article{SDMLBDGFNDD26,
  author       = "Sorino, Paolo and De Bellis, Alessandro and Malitesta, Daniele and Lofu, Domenico and Bonfiglio, Catia and Donghia, Rossella and Giannelli, Gianluigi and Ferrara, Antonio and Narducci, Fedelucio and Di Noia, Tommaso and Di Sciascio, Eugenio",
  title        = "AMIUGraph: Analysis and Modeling of Interactions for Utility-driven Benchmarking of Graph-Based Models in Healthcare",
  journal      = "Journal of BMC Medical Informatics and Decision Making",
  year         = "2026",
  url          = "http://sisinflab.poliba.it/Publications/2026/SDMLBDGFNDD26"

}