The DOI matches its Crossref record; 118 of the 124 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207 · cited 1×
“A system presented by Healey and Picard [96] was able to classify stress during real-world driving tasks into three levels based on wearable sensors including two skin conductivity sensors, ECG, EMG, chest expansion respiration sensor.”
Flagged first, then by citations
6 of the 124 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
End-to-end design of wearable sensors
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
The emergence of AI-based wearable sensors for digital health technology: a review
Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535
“This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12–15].”
BackgroundA survey on wearable sensor modality centred human activity recognition in health care
Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409
Hybrid multimodal wearable sensors for comprehensive health monitoring
Mahato, K., Saha, T., Ding, S., Sandhu, S. S., Chang, A., & Wang, J. (2024) · Nature Electronics · doi:10.1038/s41928-024-01247-4 · Cited by 482
“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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
