Wearable Biosensor Methodologies for Disease Diagnosis and Health Monitoring
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 330
Special Issue Editor
Interests: (bio)signal processing; machine learning; brain computer interface; health monitoring systems; mathematical models; control robot
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in wearable biosensors and intelligent signal analytics have transformed the landscape of disease diagnosis and health monitoring. The continuous, non-invasive acquisition of physiological signals—such as EEG, ECG, EMG, PPG, GSR, respiration, and motion data—enables real-time assessments of human health in everyday environments. Coupled with modern machine learning and artificial intelligence, wearable technologies are emerging as powerful tools for early disease detection, brain disorders, personalized treatment, and preventive medicine.
Unlike conventional hospital-based measurements, wearable biosensing systems provide long-term and context-aware monitoring. However, the translation of raw biosignals into reliable clinical indicators remains a major scientific challenge. Key methodological issues include noise-robust preprocessing, multimodal sensor fusion, feature learning from small and imbalanced datasets, explainable AI for medical decision-making, and validation in real-world settings.
Mathematical and computational pipelines for wearable health analytics typically involve:
- Signal quality assessment and artifact removal;
- Time–frequency and nonlinear feature extraction;
- Feature selection and dimensionality reduction;
- Machine/deep learning-based modeling;
- Clinical interpretation and statistical validation.
This Special Issue aims to bring together innovative research on wearable biosensor methodologies that advance the diagnosis, prognosis, and monitoring of diseases such as cardiovascular disorders, neurological conditions, mental health (ADHD, Autism), sleep abnormalities, and rehabilitation and health monitoring. We particularly welcome interdisciplinary studies that combine hardware design, signal processing, and AI with clinical validations.
Manuscripts are invited on, but not limited to, the following themes:
- Novel wearable biosensor designs for physiological monitoring;
- AI and deep learning for ECG/EEG/EMG/PPG/GSR analysis;
- Multimodal sensor fusion for disease diagnosis;
- Early detection of cardiovascular and neurological disorders;
- Stress, fatigue, and mental health assessment using wearables;
- Edge AI and embedded analytics for real-time monitoring;
- Robust preprocessing and artifact removal in ambulatory recordings;
- Explainable and trustworthy AI in digital health;
- Personalized and adaptive health monitoring systems;
- Clinical validation of wearable-based biomarkers;
- Remote patient monitoring and telemedicine applications;
- Biosignal-based human–machine interaction and rehabilitation.
The final goal of this Special Issue is to accelerate the translation of wearable biosensing technologies into reliable clinical and real-world health solutions.
Dr. Amin Hekmatmanesh
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- brain–computer interface (BCI)
- electroencephalogram (EEG)
- machine learning
- biomedical signal processing
- brain computer interface
- health monitoring
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