A Framework for Evaluating the Reliability of Health Monitoring Technologies Based on Ambient Intelligence
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| Publicado en: | Information vol. 16, no. 10 (2025), p. 833-849 |
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MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Shakes, Scott Mfundo |u Department of Computer Science, University of Fort Hare, Alice 5700, South Africa; sscott@ufh.ac.za | |
| 245 | 1 | |a A Framework for Evaluating the Reliability of Health Monitoring Technologies Based on Ambient Intelligence | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The reliability of health monitoring technologies has become increasingly critical as Ambient Intelligence (AmI) becomes integrated into healthcare. However, a significant gap remains in systematically evaluating reliability, particularly in resource-constrained environments. This study addresses this gap by introducing a comprehensive framework specifically designed to evaluate the reliability of AmI-based health monitoring systems. The proposed framework combines robust simulation-based techniques, including reliability block diagrams (RBDs) and Monte Carlo Markov Chain (MCMC), to evaluate system robustness, data integrity, and adaptability. Validation was performed using real-world continuous glucose monitoring (CGM) and heart rate monitoring (HRM) systems in elderly care. The results demonstrate that the framework successfully identifies critical vulnerabilities, such as rapid initial system degradation and notable connectivity disruptions, and effectively guides targeted interventions that significantly enhance overall system reliability and user trust. The findings contribute actionable insights for practitioners, developers, and policymakers, laying a robust foundation for further advancements in explainable AI, proactive reliability management, and broader applications of AmI technologies in healthcare. | |
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Patient safety | ||
| 653 | |a Failure | ||
| 653 | |a Markov chains | ||
| 653 | |a System reliability | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Health care | ||
| 653 | |a Heart rate | ||
| 653 | |a Glucose monitoring | ||
| 653 | |a Telemedicine | ||
| 653 | |a Adaptive technology | ||
| 653 | |a Sensors | ||
| 653 | |a Elder care | ||
| 653 | |a Wearable computers | ||
| 653 | |a Connectivity | ||
| 653 | |a Block diagrams | ||
| 653 | |a Ambient intelligence | ||
| 653 | |a Monitoring systems | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Robustness | ||
| 653 | |a Markov analysis | ||
| 653 | |a Case studies | ||
| 700 | 1 | |a Nobert, Jere |u Department of Computer Science, University of Fort Hare, Alice 5700, South Africa; sscott@ufh.ac.za | |
| 700 | 1 | |a Sibanda Khulumani |u Department of Applied Informatics and Mathematical Sciences, Walter Sisulu University, East London 5200, South Africa; ksibanda@wsu.ac.za | |
| 700 | 1 | |a Domor, Mienye Ibomoiye |u Center for Artificial Intelligence and Multidisciplinary Innovations, Department of Auditing, College of Accounting Sciences, University of South Africa, Pretoria 0002, South Africa; emienyid@unisa.ac.za | |
| 773 | 0 | |t Information |g vol. 16, no. 10 (2025), p. 833-849 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265911745/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265911745/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265911745/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |