Gaussian processes for personalized e-health monitoring with wearable sensors
Abstract
Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into "intelligent" analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.
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Some papers find irregular patterns in vital signs time series such as abnormal episodes in ECG pulses, SpO2 signal, and blood glucose level, mostly discovering unusual temporal patterns in continuous data.
“Some of the papers considered finding irregular patterns in vital signs time series such as abnormal episodes in ECG pulses [31,32], SpO2 signal [33] and blood glucose level [28], which mostly discover unusual temporal patterns in continuous data.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [31]
Most papers using anomaly detection usually deal with short-term and multivariate data sets to characterize entire data to find discords.
“According to our review, most of the papers using the anomaly detection approach usually deal with short term [30] and multivariate data sets [31] in order to characterize the entire the data to find discords.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [31]
Several studies in this area have used stored clinical data sets.
“several studies in this area have used the stored clinical data sets [31,89].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [31]
Due to the nature of vital signs, some are essentially counted as continuous such as ECG, heart rate and respiration rate, or discrete like blood glucose level.
“Due to the nature of vital signs, some of them are essentially counted as continuous such as ECG, heart rate and respiration rate [31,42] or discrete like blood glucose level [28].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [31]
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
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The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727 · see citing works
Wearable Sensors for Human Activity Monitoring: A Review
Mukhopadhyay, S. C. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2370945 · Cited by 1,401 · see citing works
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542 · see citing works
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Clifton, L., Clifton, D. A., Pimentel, M. A. F., Watkinson, P. J., & Tarassenko, L.
- Affiliations
- University of Oxford (GB)
- Oxford University Hospitals NHS Trust (GB)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2013-01
- Volume
- 60
- Issue
- 1
- Pages
- 193-197
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2029887827
- Cited by
- 117
- References
- 1
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 7 · 2022: 5 · 2021: 13 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 9 · 2016: 16 · 2015: 14 · 2014: 2 · 2013: 6 · 2012: 1
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (WT 088877/Z/09/Z)
- Engineering and Physical Sciences Research Council (EP/F058845/1, EP/H019944/1, 985352)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Gaussian processes, wearable sensors, patient monitoring, sensor artifacts, data uncertainty, data incompleteness, false alarm reduction, personalized healthcare, clinical deployment
- MeSH terms
- Clothing, Humans, Regression Analysis, Normal Distribution, Telemedicine, Monitoring, Ambulatory, Precision Medicine
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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.
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