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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 2282

Special Issue Editor


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Guest Editor
Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland
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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Published Papers (2 papers)

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Research

18 pages, 836 KB  
Article
Use of Digital Biomarkers from Sensing Technologies to Explore the Relationship Between Daytime Activity Levels and Sleep Quality in Nursing Home Residents with Dementia: A Proof-of-Concept Study
by Lydia D. Boyle, Monica Patrascu, Bettina S. Husebo, Ole Martin Steihaug, Kristoffer Haugarvoll and Brice Marty
Sensors 2025, 25(21), 6635; https://doi.org/10.3390/s25216635 - 29 Oct 2025
Viewed by 926
Abstract
Inactivity and increases in psychological and behavioral symptoms are common for people with dementia, and current assessment relies on proxy-rated tools. We investigate the feasibility and adherence of the use of sensor technology by exploring the relationship between daytime activity and sleep quality. [...] Read more.
Inactivity and increases in psychological and behavioral symptoms are common for people with dementia, and current assessment relies on proxy-rated tools. We investigate the feasibility and adherence of the use of sensor technology by exploring the relationship between daytime activity and sleep quality. For a total of 42 day–night data pairs in nursing home residents with dementia (N = 11), Garmin Vivoactive5 and Somnofy monitored continuous physical activity levels, sleep efficiency (SE), sleep score, sleep regularity index (SRI), and wake after sleep onset (WASO). Using the Spearman coefficient, we explored correlations between digital and proxy-rated tools (Personal Self Maintenance Scale (PSMS) and Neuropsychiatric Inventory–Nursing Home version (NPI-NH)) and the relationships between the digital biomarkers (SE, SRI, WASO, sleep score, physical activity). Participants (mean age 84 years) had moderate to severe degrees of dementia. Daytime activity levels correlated to sleep quality parameters WASO (−0.34, p = 0.03), and SRI (0.43, p = 0.01), and traditional sleep measures were associated with digital biomarkers (WASO/NPI-NH-K, p = 0.03). We found a relationship between daytime activity and sleep quality; however, the bidirectional relationship remains ambiguous and should be further investigated. The use of sensing technologies for people with dementia residing in a nursing home is feasible, although not without limitations, and has the potential to identify subtle changes, improving clinical assessment and the corresponding care recommendations. Full article
(This article belongs to the Special Issue Wearable Sensors and Human Activity Recognition in Health Research)
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14 pages, 930 KB  
Article
Acute Effects of Complex Hand Proprioceptive Task on Low-Frequency Hand Rest Tremor
by Francesca Di Rocco, Emanuel Festino, Olga Papale, Marianna De Maio, Cristina Cortis and Andrea Fusco
Sensors 2025, 25(20), 6502; https://doi.org/10.3390/s25206502 - 21 Oct 2025
Viewed by 927
Abstract
Resting hand tremor is a low-frequency, involuntary oscillation influenced by mechanical and neural factors, often manifesting as inter-limb asymmetry. Therefore, the aim of this study was to investigate whether a single complex hand proprioceptive task can acutely modulate tremor in healthy young adults [...] Read more.
Resting hand tremor is a low-frequency, involuntary oscillation influenced by mechanical and neural factors, often manifesting as inter-limb asymmetry. Therefore, the aim of this study was to investigate whether a single complex hand proprioceptive task can acutely modulate tremor in healthy young adults and whether it can induce asymmetry between limbs. Fifty participants (age: 25.0 ± 2.5 years) completed a 40-min proprioceptive task (anteroposterior, mediolateral, clockwise, and counterclockwise), with bilateral resting tremor recorded via triaxial accelerometry before and immediately after the intervention on both dominant and non-dominant limbs. Frequency-domain analysis showed a significant (p < 0.001) increase in tremor amplitude and a small decrease in mean frequency in the 2–4 Hz band immediately after the complex hand proprioceptive task for both limbs. These findings provide novel evidence that a single, wearable-based protocol can transiently modulate tremor dynamics, supporting the use of a non-invasive tool for neuromuscular monitoring in sport, rehabilitation, and clinical practice. Full article
(This article belongs to the Special Issue Wearable Sensors and Human Activity Recognition in Health Research)
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