Wearable Sensors and Human Activity Recognition in Health Research
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 41
Special Issue Editor
Interests: human activity recognition; physical activity; digital phenotyping; signal processing; digital health technologies
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The widespread adoption of wearable sensors offers an unprecedented opportunity to objectively measure human activity and behavior in health research. Human activity recognition (HAR) methods derived from accelerometers and other body-worn devices are now central to studies of physical function, chronic disease, mental health, aging, and more. However, this field faces major challenges in developing methods that are generalizable across populations, sensor types, and body locations, and reproducible in real-world conditions.
In this Special Issue, we seek contributions that explore algorithm development, validation, and deployment of HAR models in diverse real-world contexts. We especially encourage studies that address scalability of algorithms to free-living environments, robust model training across heterogeneous datasets, and open-source pipelines that support reproducibility. Contributions that advance best practices in labeling, device calibration, feature extraction, and activity classification across populations are also welcome.
Submissions may include original research, systematic reviews, or methodological papers that contribute to scalable, interpretable, and clinically relevant HAR approaches in health research.
We look forward to your contributions.
Dr. Marcin Strączkiewicz
Guest Editor
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Keywords
- wearable sensors
- human activity recognition
- accelerometry
- generalization
- reproducibility
- free-living environments
- machine learning
- open science
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