Sensing Technologies for Mobile Health Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 20 October 2026 | Viewed by 669
Special Issue Editors
Interests: digital health; mobile health; remote monitoring; biostatistics
Interests: health data science; bedside and remote patient monitoring; wearable computing; cardiovascular monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in sensing technologies, mobile computing, and embedded AI systems have transformed the landscape of digital health. Wearable devices, smartphones, and ambient sensors now enable continuous, real-world monitoring of physiological, behavioral, and environmental patterns at an unprecedented scale. These sensing systems provide valuable opportunities for early disease detection, personalized intervention, remote healthcare, and population-level health analytics. Yet significant challenges remain, including multimodal data integration, signal quality and reliability, algorithmic robustness in real-world conditions, privacy preservation, and clinical validation.
This Special Issue welcomes original research and reviews that advance the development, integration, and application of sensing technologies for mobile health monitoring. Topics of interest include, but are not limited to, the following:
- Physiological, behavioral, and environmental sensing;
- Wearable and smartphone-based health analytics;
- Multimodal signal processing and data fusion;
- Mobile sensing for mental and physical health assessment;
- Machine learning and AI for real-world health monitoring;
- Privacy, security, and ethical considerations;
- Clinical translation and validation of mobile sensing systems.
Dr. Yuezhou Zhang
Dr. Shaoxiong Sun
Guest Editors
Manuscript Submission Information
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Keywords
- mobile health (mHealth)
- wearable sensors
- smartphones
- remote monitoring
- behavioral sensing
- loT
- multimodal signal processing
- machine learning for health
- digital biomarkers
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