ArticleIEEE internet of things journal2024
Engagement-free and Contactless Bed Occupancy and Vital Signs Monitoring.
Article in IEEE internet of things journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Unobtrusive Sensing at Home Towards Healthcare 5.0: Technologies, Applications, and Future Directions.Biosensors · 2026Review
- Adaptive physiology-informed correction for reliable remote photoplethysmography heart-rate monitoring.NPJ digital medicine · 2026Article
- A Testbed for the Development and Validation of Contactless Vital Signs Monitoring Systems.Sensors (Basel, Switzerland) · 2026Article
- Clinical Application of Monitoring Vital Signs in Dogs Through Ballistocardiography (BCG).Veterinary sciences · 2025Article
- Poster:A Contactless Health Monitoring System for Humans and Animals.Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems · 2024Article
- Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.