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Activity-Aware Mental Stress Detection Using Physiological Sensors
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“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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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