Activity-Aware Mental Stress Detection Using Physiological Sensors
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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 [39]
For ECG signals, although Bsoul et al. proposed dozens of features, the main focus of research is to consider R points and their properties such as R-R intervals and pick counts.
“As an example, for the works studying ECG signals, although Bsoul et al. [65] have proposed dozens of features, the main focus of research is to consider R points (the pick point of each beat), and its properties in ECG pulses such as R-R intervals, pick counts, etc. [39,64].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [39]
Bayesian networks, fuzzy state machines, logistic regression models, and association rules have been used for specific health monitoring tasks.
“Some other examples are mentioned here: Bayesian network for classifying stress level [26,39] or for predicting patient's state [72], Fuzzy state machine for estimating the health status to provide alerts [68], logistic regression models [42] and association rules [41] to predict stress level and hospitalization of hemodialysis patients, respectively.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [39]
Other general statistical parameters and probability functions have been used for data mining techniques in the literature.
“Aside from the above mentioned techniques, other general statistical parameters and probability functions have been used for data mining techniques in the literature [39,53].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [39]
Some research has been done on unlabeled data to consider uncontrolled situations, especially for experimental data sets.
“Some researches have been done on unlabeled data in order to consider uncontrolled situations for especially experimental data sets [39,44,88].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [39]
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- Authors
- Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., & Griss, M.
- Affiliations
- Carnegie Mellon University (US)
- Nokia (Finland) (FI)
- Venue
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 211-230
- ISSN
- 1867-8211 (ISSN-L)
- 1867-822X
- OpenAlex
- W1912166081
- Cited by
- 292
- References
- 42
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 9 · 2023: 11 · 2022: 15 · 2021: 23 · 2020: 29 · 2019: 33 · 2018: 41 · 2017: 31 · 2016: 31 · 2015: 23 · 2014: 13 · 2013: 13 · 2012: 9
- FWCI
- 34.72
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (publishedVersion)
- KiltHub Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- mental stress detection, physiological sensors, electrocardiogram, skin conductance, accelerometer, physical activity, stress classification, wearable sensors
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