Mobile Health: Sensors, Analytic Methods, and Applications
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many real-world collection scenarios introduce different sources of noise that degrade data quality, such as electromagnetic interference or uncertainty in task scheduling for devices that perform sampling.
“Many real-world collection scenarios introduce different sources of noise that degrade data quality, such as electromagnetic interference or uncertainty in task scheduling for devices that perform sampling [271].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [271]
Optical sensors are immune to electromagnetic interference and electrically safe.
“They are immune to electromagnetic interference, electrically safe, can achieve outstanding miniaturization, and they are capable of capturing nanoscale volumes, allowing the non-invasive examination of biological matter with relatively large penetration depths.”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [25]
Optical sensing elements are often inexpensive, water- and corrosion-resistant.
“Sensing elements, whether in the form of optical fibers or optically transparent encapsulated photoplethysmographic and biochemical sensors, are often inexpensive, water- and corrosion-resistant [25].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [25]
Optical sensors can achieve outstanding miniaturization and capture nanoscale volumes, allowing non-invasive examination of biological matter with relatively large penetration depths.
“They are immune to electromagnetic interference, electrically safe, can achieve outstanding miniaturization, and they are capable of capturing nanoscale volumes, allowing the non-invasive examination of biological matter with relatively large penetration depths.”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [25]
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
- Authors
- Kumar, S., Murphy, S. A., & Rehg, J. M.
- Affiliations
- Georgia Institute of Technology (US)
- University of Michigan (US)
- University of Memphis (US)
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2017
- OpenAlex
- W4235407935
- Cited by
- 38
- References
- 0
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 2 · 2022: 8 · 2021: 3 · 2020: 5 · 2019: 6 · 2018: 1 · 2017: 1
- FWCI
- 3.87
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- mobile health, on-body sensors, health behavior monitoring, health prediction, mobile-based interventions, health outcomes, wearable sensors
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.
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