Experimental Study of the Stress Level at the Workplace Using an Smart Testbed of Wireless Sensor Networks and Ambient Intelligence Techniques
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Predictive models have been used to predict further stress levels.
“As the common examples of the predictive models, authors in [38,39] presented a method which predicts the further stress levels of a subject.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [38]
In most research in health monitoring systems, simple statistical parameters or statistical functions are adequate to formulate the data and retrieve expected information.
“In the most of research in health monitoring systems, dealing with the simple statistical parameters (e.g., mean, variance etc.) or statistical functions e.g., risk function [61], factor analysis [68]) would be adequate to formulate the data and retrieve the expected information [38].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [38]
Papers that have developed health monitoring systems have mostly used their own data gathering experiments to design, model and test the data analysis step.
“The papers which have developed the health monitoring systems have mostly used their own data gathering experiments to design, model and test the data analysis step [26,38,88].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [38]
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A Comprehensive Survey of Wireless Body Area Networks: On PHY, MAC, and Network Layers Solutions
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Silva, F., Olivares, T., Royo, F., Vergara, M. A., & Analide, C.
- Affiliations
- University of Minho (PT)
- University of Castilla-La Mancha (ES)
- Venue
- Lecture notes in computer science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 200-209
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1918707839
- Cited by
- 8
- References
- 13
- Citations by year
- 2019: 2 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 2 · 2013: 1
- FWCI
- 0.85
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Lecture notes in computer science (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- occupational stress, stress prediction, ambient intelligence, wireless sensor networks, pulse sensor, heart rate monitoring, environmental monitoring, quality of life
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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.
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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