The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Activity-Aware Mental Stress Detection Using Physiological Sensors
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Studies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,615
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION, DATA MINING AND KNOWLEDGE DISCOVERY
Burger, C. J. C. (1998) · Cited by 3,261
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207
Lim, T.-S., Loh, W., & Shih, Y. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,130
Cited by
Sorted by citations
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection
Schmidt, P., Reiss, A., Dürichen, R., Marberger, C., & Laerhoven, K. V. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242985 · Cited by 1,508
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques
Gedam, S., & Paul, S. (2021) · IEEE Access · doi:10.1109/access.2021.3085502 · Cited by 521
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452
Advanced internet of things for personalised healthcare systems: A survey
Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2017.06.018 · Cited by 414
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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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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