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
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
Boucsein, W. (1992) · doi:10.1007/978-1-4757-5093-5 · Cited by 818
Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability
Vrijkotte, T. G. M., van Doornen, L. J. P., & de Geus, E. J. C. (2000) · Hypertension · doi:10.1161/01.hyp.35.4.880 · Cited by 756
Discriminating Stress From Cognitive Load Using a Wearable EDA Device
Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., & Ehlert, U. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2036164 · Cited by 742
Dedovic, K., Renwick, R., Khalili‐Mahani, N., Engert, V., LUPIEN, S. J., & Pruessner, J. C. (2005) · Journal of Psychiatry and Neuroscience · doi:10.1139/jpn.0541 · Cited by 737
(1954) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1954.tb15286.x · Cited by 687
(1984) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a061650 · Cited by 664
Cannon, W. B. (1915) · doi:10.1037/10013-000 · Cited by 621
Influence of Mental Stress on Heart Rate and Heart Rate Variability
Taelman, J., Vandeput, S., Spaepen, A. J., & Huffel, S. V. (2009) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-540-89208-3_324 · Cited by 562
Psychophysiological stress and emg activity of the trapezius muscle
Lundberg, U., Kadefors, R., Melin, B., Palmerud, G., Hassmén, P., Engström, M., & Elfsberg Dohns, I. (1994) · International Journal of Behavioral Medicine · doi:10.1207/s15327558ijbm0104_5 · Cited by 381
Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings.
Salahuddin, L., Cho, J., Jeong, M. G., & Kim, D. (2007) · 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2007.4353378 · Cited by 374
Biomedical Sciences Instrumentation
(2021) · Biomedical Sciences Instrumentation · doi:10.34107/biomedsciinstrum · Cited by 317
Zhai, J., & Barreto, A. B. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.259421 · Cited by 306
Role of Stress in Functional Gastrointestinal Disorders
Mönnikes, H., Tebbe, J. J., Hildebrandt, M., Arck, P., Osmanoglou, E., Rose, M., Klapp, B., Wiedenmann, B., & Heymann-Mönnikes, I. (2001) · Digestive Diseases · doi:10.1159/000050681 · Cited by 298
Bernardi, L. (1996) · Cardiovascular Research · doi:10.1016/0008-6363(96)00081-8 · Cited by 253
Mental stress as a causal factor in the development of hypertension and cardiovascular disease
Pickering, T. G. (2001) · Current Hypertension Reports · doi:10.1007/s11906-001-0047-1 · Cited by 235
Fortnightly review: Stress, the brain, and mental illness
Herbert, J. (1997) · BMJ · doi:10.1136/bmj.315.7107.530 · Cited by 146
(1970) · American Heart Journal · doi:10.1016/0002-8703(70)90026-8 · Cited by 144
Biomedical sciences instrumentation
Cheng, G. C. (1970) · Computers in Biology and Medicine · doi:10.1016/0010-4825(70)90008-9 · Cited by 124
Spectral Analysis of Heart Rate Variability during Exercise in Trained Subjects
Pichon, A., de Bisschop, C., Manuel Roulaud, B., Denjean, A., & Papelier, Y. (2004) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000142403.93205.35 · Cited by 118
Human Activity Recognition: Accuracy across Common Locations for Wearable Sensors
õn, D. O. ́., & õn, O. ́. (2006) · Cited by 113
THE RATIONALE FOR TREATING THE CHANGE IN GALVANIC SKIN RESPONSE AS A CHANGE IN CONDUCTANCE
Darrow, C. W. (1964) · Psychophysiology · doi:10.1111/j.1469-8986.1964.tb02618.x · Cited by 95
Activity-aware ECG-based patient authentication for remote health monitoring
Sriram, J., Shin, M., Choudhury, T., & Kotz, D. F. (2009) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1647314.1647378 · Cited by 85
Mental Stress and Coronary Artery Disease: A Multidisciplinary Guide
Holmes, S. D., Krantz, D. S., Rogers, H. L., Gottdiener, J. S., & Contrada, R. J. (2006) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2006.08.013 · Cited by 78
Breslau, N., Kessler, R. C., & Peterson, E. L. (1998) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.41 · Cited by 52
Distinguishing emotional from physical activation in ambulatory psychophysiological monitoring
Wilhelm, F. H., Pfaltz, M. C., Grossman, P., & Roth, W. T. (2006) · PubMed · doi:10.5451/unibas-ep9413 · Cited by 47
Analysis of galvanic skin responses with principal components and clustering techniques
Tarvainen, M. P., Koistinen, A. S., Valkonen‐Korhonen, M., Partanen, J. V., & Karjalainen, P. A. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.951509 · Cited by 47
Effect of Movements on the Electrodermal Response after a Startle Event
Bächlin, M., Setz, C., Bert Arnrich, A., Roggen, D., Tröster, G., & Schumm, J. (2008) · Methods of Information in Medicine · doi:10.3414/me9108 · Cited by 38
Zhai, J., & Barreto, A. B. (2006) · Conference proceedings · doi:10.1109/iembs.2006.4397662 · Cited by 36
Effect of movements on the electrodermal response after a startle event
Schumm, J., Bächlin, M., Setz, C., Bert Arnrich, A., Roggen, D., & Tröster, G. (2008) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1109/pcthealth.2008.4571101 · Cited by 19
van Steenis, H. G., & Tulen, J. H. M. (2002) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.1997.754476 · Cited by 9
Effect of movements on the electrodermal response after a startle event
Schumm, J., Bächlin, M., Setz, C., et al. (2008) · doi:10.4108/icst.pervasivehealth2008.2529 · Cited by 4
Psychophysiological Body Activation Characteristics in Daily Routines
Kusserow, M., Amft, O., & Tröster, G. (2009) · International Symposium on Wearable Computers · doi:10.1109/iswc.2009.10 · Cited by 3
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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
