Contactless Technologies for Patient Health Monitoring

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2695

Special Issue Editors


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Guest Editor
Philips, Intellectual Property & Standards, Eindhoven, The Netherlands
Interests: contactless monitoring; patient monitoring; camera-augmented clinical decision support
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Philips, Hospital Patient Monitoring, Eindhoven, The Netherlands
Interests: contactless monitoring; tissue optics; skin pigmentation
Special Issues, Collections and Topics in MDPI journals
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: intelligent health monitoring; unobtrusive sensing; audio/video analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Philips, Intellectual Property & Standards, Eindhoven, The Netherlands
2. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: patient monitoring; contactless monitoring; clinical decision support

Special Issue Information

Dear Colleagues,

Contactless technologies such as camera and RF are transforming patient health monitoring by enabling the non-invasive measurement of a growing range of physiological and contextual parameters. Advances in artificial intelligence and hardware have expanded the scope of these technologies, making them increasingly accessible and cost-effective for both hospital and remote settings. Early adoption by healthcare providers and patients signals a trend toward their broader integration into treatment, with anticipated benefits including improved access to care, streamlined clinical workflows, and enhanced patient comfort.

This Special Issue of Bioengineering, entitled “Contactless Technologies for Patient Health Monitoring”, seeks high-quality original research and comprehensive reviews in this rapidly evolving field. Key topics include the following:

  • Innovative methods and algorithms for contactless measurement of physiological signals (e.g., heart/pulse rate (variability), respiration, blood oxygen saturation, blood pressure, body temperature, tissue perfusion);
  • Techniques for contactless contextual monitoring (e.g., movement analysis, pose estimation, patient presence detection, fall detection/prevention, workflow visualization);
  • Development of novel systems and devices (e.g., time-of-flight sensors, thermal imaging, VR/AR, multispectral, smartphone-based, radar, WiFi, passive infrared, audio), including multimodal and hybrid (contact and contactless) approaches;
  • Clinical validation studies in diverse environments such as (neonatal) intensive care, general wards, sleep monitoring, and imaging support;
  • Applications in telehealth, screening, clinical decision support, home monitoring, mental health assessment, affective computing, and security;
  • New benchmarks, datasets, literature reviews, and protocols supporting the advancement of contactless health monitoring technologies.

Researchers are encouraged to contribute studies that advance the knowledge and application of contactless patient monitoring.

Dr. Mark van Gastel
Dr. Wim Verkruysse
Dr. Xi Long
Dr. Rick Bezemer
Guest Editors

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Keywords

  • contactless monitoring
  • unobtrusive sensing
  • physiological measurements
  • contextual monitoring
  • clinical decision support

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Published Papers (2 papers)

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Research

29 pages, 4988 KB  
Article
MARU-MTL: A Mamba-Enhanced Multi-Task Learning Framework for Continuous Blood Pressure Estimation Using Radar Pulse Waves
by Jinke Xie, Juhua Huang, Chongnan Xu, Hongtao Wan, Xuetao Zuo and Guanfang Dong
Bioengineering 2026, 13(3), 320; https://doi.org/10.3390/bioengineering13030320 - 11 Mar 2026
Cited by 1 | Viewed by 707
Abstract
Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. [...] Read more.
Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. However, radar pulse wave (RPW) signals are susceptible to motion artifacts, respiratory interference, and environmental clutter, posing persistent challenges to estimation accuracy and robustness. In this paper, we propose MARU-MTL, a Mamba-enhanced multi-task learning framework for continuous BP estimation using a single millimeter-wave radar sensor. To address signal quality degradation, a Variational Autoencoder-based Signal Quality Index (VAE-SQI) mechanism is proposed to automatically screen RPW segments without manual annotation. To capture long-range temporal dependencies across cardiac cycles, we integrate a Bidirectional Mamba module into the bottleneck of a U-Net backbone, enabling linear-time sequence modeling with respect to the segment length. We also introduce a multi-task learning strategy that couples BP regression with arterial blood pressure waveform reconstruction to strengthen physiological consistency. Extensive experiments on two datasets comprising 55 subjects demonstrate that MARU-MTL achieves mean absolute errors of 3.87 mmHg and 2.93 mmHg for systolic and diastolic BP, respectively, meeting commonly used AAMI error thresholds and achieving metrics comparable to BHS Grade A. Full article
(This article belongs to the Special Issue Contactless Technologies for Patient Health Monitoring)
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14 pages, 2998 KB  
Article
Clinical Validation of rPPG-Enabled Contactless Pulse Rate Monitoring Software in Cardiovascular Disease Patients
by Jing Wei Chin, Po Him David Chan, Shutao Chen, Chun Hong Cheng, Richard H. Y. So, Elaine Chow, Benny S. P. Fok and Kwan Long Wong
Bioengineering 2026, 13(2), 246; https://doi.org/10.3390/bioengineering13020246 - 20 Feb 2026
Viewed by 1392
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide, creating demand for continuous, unobtrusive monitoring solutions. This clinical validation evaluates the accuracy of remote photoplethysmography (rPPG), a contactless method using camera video, for measuring pulse rate (PR) in patients with CVD. [...] Read more.
Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide, creating demand for continuous, unobtrusive monitoring solutions. This clinical validation evaluates the accuracy of remote photoplethysmography (rPPG), a contactless method using camera video, for measuring pulse rate (PR) in patients with CVD. Methods: We enrolled 50 adults with confirmed CVD at a clinical trial center. In a 6 min rested session, synchronized facial video (under controlled lighting), electrocardiogram (ECG), and photoplethysmography (PPG) signals were recorded. PR was derived from 25 s video segments using rPPG-enabled software and compared to ECG-derived PR via regression and Bland–Altman analysis. Results: Data from 47 participants (n = 817 samples) were analyzed. rPPG-derived PR showed strong agreement with ECG, with a mean absolute error of 1.061 bpm, root-mean-squared error of 2.845 bpm, and Pearson correlation of 0.962. Mixed-effects regression analyses (after 2% outlier removal, n = 782) indicated minimal influence from demographic, environmental, or CVD factors on accuracy. PPG-ECG discrepancies reflected inherent methodological differences. Conclusion: The rPPG method provides accurate, contactless PR monitoring in CVD patients, supporting its potential for remote patient monitoring and early deterioration detection. Future work will validate rPPG for irregular rhythms, additional vital signs, and diverse cohorts to strengthen clinical robustness for cardiometabolic risk assessment. Full article
(This article belongs to the Special Issue Contactless Technologies for Patient Health Monitoring)
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