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Sensors for Human Health Monitoring Based on Biomedical Signals: From New Perception to Intelligent Diagnosis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 2320

Special Issue Editor


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Guest Editor
College of Bioengineering, Chongqing University, Chongqing 400044, China
Interests: detection and processing of biomedical information; methods and equipment for noninvasive detection, diagnosis and treatment analysis

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore innovative sensing technologies, methods, and approaches applied throughout the entire chain of human health and disease monitoring (including, but not limited to, physiological signal acquisition, pathological feature identification, disease risk warning, treatment response evaluation, and rehabilitation tracking). The core focus is on utilizing biomedical signals (such as electrophysiological, biochemical, mechanical, optical, acoustic, etc.) for non-invasive/minimally invasive, continuous, and dynamic monitoring, and exploring how advanced signal processing, data fusion, and artificial intelligence technologies can enhance the quality, interpretability, and ultimately the level of intelligent assistance in diagnosis of sensor data.

Potential topics include, but are not limited to, the following:

  • New sensor design and materials: such as flexible/wearable/injectable/swallowable sensors, micro/nano sensors, biocompatible materials, multifunctional integrated sensors, etc.
  • Innovative signal acquisition and enhancement methods: such as weak signal detection, anti-interference/noise reduction technology, multi-modal signal synchronous acquisition, non-contact sensing (like radar, optical), and novel biosensor detection, etc.
  • Advanced signal processing and analysis algorithmsThe application of artificial intelligence (deep learning, machine learning), big data analysis, feature extraction and selection, pattern recognition, and personalized modeling in the analysis of biomedical signals.
  • Intelligent diagnosis and decision supportSuch as disease early warning models based on sensor data, auxiliary diagnostic algorithms, treatment efficacy evaluation models, and personalized health management plan generation.
  • System integration and clinical application validation: The development of wearable/portable/home monitoring systems, integration of sensors-edge computing-cloud platforms, solutions tailored for specific diseases (cardiovascular, neurodegenerative, metabolic, mental health, etc.), and their preclinical/clinical validation.

Prof. Dr. Zhong Ji
Guest Editor

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Keywords

  • biomedical signals
  • health monitoring
  • sensors
  • wearable devices
  • flexible electronics
  • signal processing
  • artificial intelligence
  • intelligent diagnosis
  • non-invasive monitoring
  • physiological parameters monitoring

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Published Papers (2 papers)

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Research

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22 pages, 4372 KB  
Article
Suppressing Non-Stationary Motion Artefacts in Mobile EEG Using Generalized Eigenvalue Decomposition
by Mohammad Khazaei, Khadijeh Raeisi, Patrique Fiedler, Pierpaolo Croce, Filippo Zappasodi and Silvia Comani
Sensors 2026, 26(8), 2440; https://doi.org/10.3390/s26082440 - 16 Apr 2026
Viewed by 296
Abstract
Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance [...] Read more.
Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance with increasing movement variability and speed. To fill this gap, we developed a method based on generalized eigenvalue decomposition (GED) to identify and suppress highly variable, non-periodic—especially transient—artefacts due to very rapid, free full body movements of different types, as they occur during sports practice. By leveraging the contrast between covariance matrices of artefactual and resting-state EEG segments, this approach isolates motion-related components for removal during multichannel EEG signal reconstruction. The method was validated on two ecological datasets featuring stereotyped head and body movements and dynamic table tennis. Comparison with state-of-the-art technique showed superior performance of our method in terms of signal-to-error ratio (SER), artefact-to-residue ratio (ARR), brain spectral power preservation and computation time. Sensitivity analysis was applied to demonstrate the method’s robustness to parameter changes. These findings highlight the potential of the proposed method as a robust, generalizable approach for motion artefact suppression in mobile EEG, particularly when applied in extreme recording conditions like during active sports activity. Full article
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Review

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23 pages, 2194 KB  
Review
AI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects
by Donghai Ye, Kehan Liu, Chenfei Luo and Ning Hu
Sensors 2026, 26(1), 146; https://doi.org/10.3390/s26010146 - 25 Dec 2025
Viewed by 1654
Abstract
Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological [...] Read more.
Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article
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