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Application of data fusion techniques and technologies for wearable health monitoring
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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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“three levels of fusion are considered for the data: 1) data level; 2) feature level; and 3) classification level [106], [107], [108], [109], [110], [111], [112].”
BackgroundMethodInfluentialPeng, M., Ning, Y., Zhang, J. W., He, Y., Xu, Z., Li, D., Yang, Y., & Ren, T.-L. (2026) · Biosensors · doi:10.3390/bios16010046 · Cited by 16
“The “fusion” stage is typically implemented at one of three levels: data-level, feature-level, and decision-level fusion (Figure 4c) [125].”
Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
Zabihi, M., Bhawya, Pandya, P., Shepley, B. R., Lester, N. J., Anees, S., Bain, A. R., Rondeau‐Gagné, S., & Ahamed, M. J. (2024) · Applied Sciences · doi:10.3390/app14072842 · Cited by 12
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Survey on biometry for cognitive automotive systems
Lozoya-Santos, J. D. J., Sepúlveda-Arróniz, V., Tudón-Martínez, J. C., & Ramírez-Mendoza, R. A. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.01.007 · Cited by 12
Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers
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Representative Safety Assessment of Autonomous Vehicle for Public Transportation
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Clinical Data Fusion and Machine Learning Techniques for Smart Healthcare
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“Anam Haq et al., 2019 [12] Provides a General fusion framework based on a combination of data and interpretation to predict the type of treatment for patients with bone fractures.”
BackgroundPolymer optical fibres in healthcare: solutions, applications and implications. A perspective
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“Wearable sensors can be categorized into three categories: motion-related, biometrical, and environmental data.(20) These can be combined and fused, while application-specific”
BackgroundIntelligent Data Fusion and Multi-Agent Coordination for Target Allocation
Noh, S. (2020) · Electronics · doi:10.3390/electronics9101563 · Cited by 10
Izmailova, E. S., Middleton, D., Yunis, R., Lakeland, J., Sowalsky, K. L., Kling, J., Ritchie, A., Guo, C. C., Byrom, B., & Kern, S. (2024) · Clinical and Translational Science · doi:10.1111/cts.70054 · Cited by 9
“54 The deployment of multiple sensors, however, might increase site or participant burden, either by the need to wear those sensors on multiple body locations, or the need to charge devices frequently due to higher power consumption.”
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Validation of two hybrid approaches for clustering age-related groups based on gait kinematics data
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A Framework of Input Devices to Support Designing Composite Wearable Computers
Khalaf, A. S., Alharthi, S. A., Hamilton, B., Dolgov, I., Son, T. C., & Dugas, P. O. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49062-1_28 · Cited by 9
Machine learning-enabled IoT applications for smart healthcare monitoring systems
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Balch, A., Cardei, M. A., Kranz, S., & Doryab, A. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3743690 · Cited by 10
Contraction Artifacts on Biceps Tissue Bioimpedance Collected using Stepped-Sine Excitations
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“It has been previously shown that microsensors such as accelerometers, gyroscopes, and magnetometers can be utilized to effectively characterize sportspecific movements amongst athletes [18], [19], as well as in characterizing general physical activity in health-monitoring scenarios [20], [21].”
BackgroundElectromyography (EMG) and Hand Gripping Force During Standing and Sitting
Mahadi, M., Nazmi, N., Bani, N. A., Rahman, S. A. S. A., Noor, N. M., & Zainuddin, S. Z. A. J. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1096/1/012077 · Cited by 8
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“Approaches like King et al.'s (2017) data fusion techniques and Chung et al.'s (2019) multimodal acquisition frameworks have laid the ground-work.”
Multimodal data fusion techniques in smart healthcare
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“The authors in [7] outline significant contributions in the application of data fusion techniques for wearable health monitoring, emphasizing advancements in sensor technology, data fusion models, and their implications for health monitoring applications.”
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“yk = τ (∑l j=0 wjkhj ) = τ (∑l j=0 wjk { τ (∑n i=0 vijxi )}) , (2)”
BackgroundK-boyutlu ağaç ve uyarlanabilir yarıçap (KD-AR Stream) tabanlı gerçek zamanlı akan veri kümeleme
Şenol, A., & Karacan, H. (2019) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.467226 · Cited by 7
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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