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Application of data fusion techniques and technologies for wearable health monitoring
Open accessAbstract
Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.
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“One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [15]
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A Device-Orientation Independent Method for Activity Recognition
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Fuzzy CARA - A Fuzzy-Based Context Reasoning System For Pervasive Healthcare
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Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease
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Ates, H. C., Nguyen, P. Q., Gonzalez-Macia, L., Morales-Narváez, E., Güder, F., Collins, J. J., & Dincer, C. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00460-x · Cited by 1,306
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“This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12–15].”
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“Reliably capturing longitudinal multi-source sensing data, and then mining it, can provide a comprehensive assessment of a person’s health status for early disease detection and improved decision-making 9,10 .”
Nweke, H. F., Wah, T. Y., Mujtaba, G., & Al-Garadi, M. A. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 408
Metadata
- Authors
- King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S.
- Affiliations
- University of Reading (GB)
- University of Exeter (GB)
- Corresponding author
- King, Rachel C.
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2017-02-23
- Volume
- 42
- Issue
- 1
- Pages
- 1-12
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W2588424569
- Cited by
- 182
- References
- 124
- Citations by year
- 2026: 18 · 2025: 10 · 2024: 15 · 2023: 22 · 2022: 25 · 2021: 24 · 2020: 20 · 2019: 24 · 2018: 20 · 2017: 4
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- CentAUR (University of Reading) (submittedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/K031910/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- data fusion, wearable sensors, health monitoring, physiological monitoring, activity monitoring, ambulatory monitoring, gait analysis, fall detection, biometric monitoring, ubiquitous health monitoring
- MeSH terms
- Wearable Electronic Devices, Algorithms, Humans, Monitoring, Physiologic, Statistics as Topic
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