Smart technologies for long-term stress monitoring at work

Stress, appraisal, and coping

Eysenck, H. J. (1985) · Behaviour Research and Therapy · doi:10.1016/0005-7967(85)90087-7 · Cited by 32,038

“Stress: appraisal and coping,”

Folkman, S. (2013) · Encyclopedia of Behavioral Medicine · doi:10.1007/978-1-4419-1005-9_215 · Cited by 27,833

Measuring emotion: The self-assessment manikin and the semantic differential

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Electrodermal Activity

Boucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526

Objective measures, sensors and computational techniques for stress recognition and classification: A survey

Sharma, N., & Gedeon, T. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.07.003 · Cited by 522

Third European Survey on Working Conditions 2000

Paoli, P. (2001) · Cited by 303

What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

Bakker, J., Pechenizkiy, M., & Sidorova, N. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.178 · Cited by 286

AffectAura: an intelligent system for emotional memory

McDuff, D., Karlson, A., Kapoor, A., Roseway, A., & Czerwinski, M. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208525 · Cited by 242

Real-Time Driver's Stress Event Detection

Rigas, G., Goletsis, Y., & Fotiadis, D. I. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2168215 · Cited by 148

Mind the body!

Sanches, P., Höök, K., Vaara, E. K., Weymann, C., Bylund, M., Ferreira, P., Peira, N., & Sjölinder, M. (2010) · Designing Interactive Systems Conference · doi:10.1145/1858171.1858182 · Cited by 130

Sensors Classification for Stress Analysis: Toward Automatic Stress Recognition

Sysoev, M., Kos, A., Sedlar, U., & Pogačnik, M. (2014) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki.2014.31 · Cited by 7

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human BehaviourPreprint

Hänsel, K., Wilde, N., Haddadi, H., & Alomainy, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.05238 · Cited by 6

Smart HRM – das „Internet der Dinge“ im Personalmanagement

Strohmeier, S., Majstorovic, D., Piazza, F., & Theres, C. (2016) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-016-0268-z · Cited by 5

Using Acceleration Data for Detecting Temporary Cognitive Overload in Health Care Exemplified Shown in a Pill Sorting Task

Kohout, L., Butz, M., & Stork, W. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00015 · Cited by 5

A novel, simple and objective method to detect movement artefacts in electrodermal activity

Thammasan, N., Stuldreher, I. V., Wismeijer, D. A., Poel, M., van Erp, J. B. F., & Brouwer, A.-M. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925512 · Cited by 5

Measuring Arousal and Emotion in Healthcare Employees Using Novel Devices

Fortune, E., Yusuf, Y., & Blocker, R. C. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00142 · Cited by 6

Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation

Bögemann, S. A., Krause, F., van Kraaij, A., Marciniak, M. A., van Leeuwen, J. M., Weermeijer, J. D. M., Mituniewicz, J., Puhlmann, L. M. C., Zerban, M., Reppmann, Z. C., et al. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02870-5 · Cited by 8

Helping Users Reflect on Their Own Health-Related Behaviors

Kocielnik, R., Hsieh, G., & Avrahami, D. (2018) · Human-computer interaction series · doi:10.1007/978-3-319-95579-7_5 · Cited by 4

BallBounce: Designing Collective Stress-Related Visualizations for Office Workers Using Galvanic Skin Response Sensor

Nkem, D., & Xue, M. (2023) · ACM Symposium on Spatial User Interaction (SUI) · doi:10.1145/3607822.3616417 · Cited by 4

Continuous Stress Detection Based on Social Media

Ding, Y., Feng, L., Cao, L., Dai, Y. Q., Wang, X., Zhang, H., Li, N., & Zeng, K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3283338 · Cited by 5

Assistive systems for quality assurance by context-aware user interfaces in health care and production

Rüther, S. (2014) · PUB – Publications at Bielefeld University (Bielefeld University) · Cited by 4

Holistic quantified-self for context-aware wearable augmented reality

Song, E., Ha, T., Park, J., Lee, H., & Woo, W. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103568 · Cited by 4

Electrodermal Activity Analysis for Training of Military Tactics

Boyce, M. W., Goldberg, B., & Moss, J. D. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601309 · Cited by 3

Human Performance-Aware Scheduling and Routing of a Multi-Skilled Workforce

van Eck, M. L., Fırat, M., Nuijten, W., Sidorova, N., & van der Aalst, W. M. P. (2017) · Complex Systems Informatics and Modeling Quarterly · doi:10.7250/csimq.2017-12.01 · Cited by 3

Enhancing Music Events Using Physiological Sensor Data

Röggla, T., Shirzadian, N., Zheng, Z., Panza, A., & Cesar, P. S. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3127919 · Cited by 3

Electrodermal activity and its effectiveness in cognitive research field

Momin, A., Shahu, A., Sanyal, S., & Chakraborty, P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819445-4.00008-4 · Cited by 3

Comparison of Several Machine Learning Classifiers for Arousal Classification: A Preliminary study

Erkuş, E. C., Purutçuoğlu, V., Arı, F., & Gökçay, D. (2020) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno50054.2020.9299316 · Cited by 3

Psychische Beanspruchung in der manuellen Montage

Tropschuh, B., Diez, M. A., Waldherr, S., & Reinhart, G. (2020) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.3139/104.112410 · Cited by 3

Characterizing electrodermal responses during sleep in a 30-day ambulatory study

Taylor, S. (2016) · Cited by 3

Self-assessment of affect-related events for physiological data collection in the wild based on appraisal theories

Niewiadomski, R., Larradet, F., Barresi, G., & Mattos, L. S. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1285690 · Cited by 2

Stress-GPT: Stress detection with an EEG-based foundation model

Lloyd, C., Lemoine, L. L., Al-Shaikh, R., Ly, K. T., Kayan, H., Perera, C., & Pham, N. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3698121 · Cited by 2

Smart HRM – das „Internet der Dinge“ im Personalmanagement

Strohmeier, S., Majstorovic, D., Piazza, F., & Theres, C. (2017) · Edition HMD · doi:10.1007/978-3-658-18165-9_9 · Cited by 2

Physiological response to acute stress against confounding factors: a white-box research method

Vila, G., Godin, C., Charbonnier, S., Sakri, O., Labyt, É., & Campagne, A. (2019) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ab360e · Cited by 2

Enabling Self-Reflection with LifelogExplorer: Generating Simple Views from Complex Data

Kocielnik, R., Maggi, F., & Sidorova, N. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/pervasivehealth.2013.251934 · Cited by 2

Prädiktion menschlicher Fehler in der Linienmontage

Klages, B., Fieg, E., Wegmann, M., & Zäh, M. F. (2024) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2024-1012 · Cited by 2

Psychische Beanspruchung in der manuellen Montage

Tropschuh, B., Diez, M. A., Waldherr, S., & Reinhart, G. (2020) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2020-1151012 · Cited by 2

Multimodal Workplace Monitoring for Human Activity Recognition

Mitsou, A., Evaggelos Spyrou, A., & Γιαννακόπουλος, Θ. (2021) · Panhellenic Conference on Informatics · doi:10.1145/3503823.3503862 · Cited by 1

Fitting the Message to the Moment: Designing Calendar-Aware Stress Messaging with Large Language Models

Rao, P., Taj, M., Mariakakis, A. T., Williams, J. J., & Bhattacharjee, A. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3737632 · Cited by 1

StressAwear - A multimodal wearable for shared awareness of stress in open plan offices

Hvilshøj, R., Hoggan, E., & Pakanen, M. (2026) · The Design Journal · doi:10.1080/14606925.2026.2614658 · Cited by 1

LifelogExplorer: A Tool for Visual Exploration of Ambulatory Skin Conductance Measurements in Context

Kocielnik, R. (2014) · Cited by 1

Wearable Devices and Smart Garments for Stress Management

Rinaldi, A., Becchimanzi, C., & Tosi, F. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-96071-5_92 · Cited by 1

Intelligent Technique for Stress Level Management for IT Employees in the Work from Home Environment using IoT Technology

Umamageswaran, J., Saravanan, K., & Srinivasan, V. P. (2023) · Cardiometry · doi:10.18137/cardiometry.2023.26.618622 · Cited by 1

Supporting Developers' Emotional Awareness: from Self-reported Emotions to Biometrics

Grassi, D. (2024) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3661167.3661209 · Cited by 1

KPI-based activity planning for people working in flexible processes

Eck, V. M. M., Sidorova, N., & van der Aalst, W. M. P. (2015) · TU/e Research Portal · Cited by 1

I Know How you Feel Now, and Here's why!: Demystifying Time-Continuous High Resolution Text-Based Affect Predictions in the Wild

Pandit, V., Schmitt, M., Cummins, N., & Schuller, B. W. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00096 · Cited by 1

Het gebruik van wearables bij chronische stress en burn-out

Custers, P., Verbrugghe, M., & Braeckman, L. (2021) · Tijdschrift voor Geneeskunde · doi:10.47671/tvg.77.21.154 · Cited by 1

Perceived distress in assisted gait with a four-wheeled rollator under stress induction conditions

Boladeras, M. D., LLanas, X. P., Fernández, M. F., Catalá, A., Urdiales, C., Musté, M., Pérez, E., & Barco, À. (2023) · Cogent Engineering · doi:10.1080/23311916.2023.2233743 · Cited by 1

Positive emotion regulation: Forecasting the future, savoring the present and reminiscing about the past

Colombo, D. (2020) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) · doi:10.6035/14109.2020.762821 · Cited by 1

A Preliminary Study on Automatic Motion Artifacts Detection in Electrodermal Activity Data Using Machine LearningPreprint

Hossain, B., Posada-Quintero, H. F., Kong, Y., McNaboe, R. Q., & Chon, K. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07650 · Cited by 1

Optiseen sensoriteknologiaan pohjautuvan pulssimittarin hyödyntäminen maksimaalisen hapenottokyvyn ja energiankulutuksen arvioinnissa

Uuskoski, M. (2015) · Jyväskylä University Digital Archive (University of Jyväskylä)

IHMISEN VISUAALINEN HAHMOTTAMISKYKY JA FYSIOLOGISEN DATAN ESITTÄMINEN

Pänkäläinen, T. (2015) · Jyväskylä University Digital Archive (University of Jyväskylä)

Nuevas técnicas de monitorización, personalización y coaching inteligente para alcanzar hábitos de vida saludables para una mejor salud cerebral

Moreno-Blanco, D. (2021) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.69176

A Transparency Statement Transforms Community-Police InteractionsPreprint

Yeager, D. S., Dobson, K. S. H., & Dittmann, A. G. (2022) · Research Square · doi:10.21203/rs.3.rs-1726104/v1

How do Para swimmers respond physiologically and cognitively when watching their performance at the Paralympic Games? A Case Study

Soares, B. B. S. B., & Winckler, C. (2023) · Cuadernos de Psicología del Deporte · doi:10.6018/cpd.499361

Monitoring and Analysis of Stress Based on Human Aura

Patil, M. S. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem24390

Wide-Range Sensor Network System by Limited Sensors Based on Various Image Analysis Method

Kubota, M., Miyaji, H., & Yamamoto, H. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00199

Evaluación de un sistema de identificación de fluctuaciones (estado ON/OFF) en enfermos de Parkinson

Barrachina, M. (2024) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.81685

Modèle Boîte-Blanche pour l'Evaluation du Stress à Partir de Mesures Physiologiques et Applicabilité en Vie Réelle

Vila, G. (2021) · theses.fr (ABES) · doi:10.70675/392ed3ffzc0a4z4036zb11ez0bb8f2b16351

Multi-modality Improves Cognitive Load Classification of Naturalistic Tasks Under Varying Signal Quality

Anders, C., Bert Arnrich, A., Real, S., Stolp, F., & Bert Arnrich, A. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-27582-0_10

Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management

Dongre, P., Neupane, S., Jadhav, P., Chandrashekar, N. D., Webb, C. A., Kumar, S., & Gračanin, D. (2026) · ACM Interactive Health Conference · doi:10.1145/3786579.3804960

Feasibility Embedded Photoplethysmogram Sensing in Short-Duration Tactile Interactions With Ubiquitous Robotics Through IMU and Confidence-Based Filtering

Datta, T., & Frederiksen, M. R. (2026) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur69537.2026.11626683

Is Stress Reactivity an Intermediary Mechanism in the Association Between NR3C1 Methylation and Adolescent Loneliness?

Koopmans, Y., Nelemans, S. A., Bijttebier, P., Bosmans, G., Noortgate, W. V. D., Leeuwen, K. G. V., & Goossens, L. (2026) · Developmental Psychobiology · doi:10.1002/dev.70190

Exploring Future Wearables for Stress Management: Insights from Value-Oriented Design Workshops

Berg, M. H., Ludden, G., Noordzij, M. L., Maeckelberghe, E. L. M., & Karahanoğlu, A. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829835

ML-Driven Framework for Accurate EDA Signal Analysis and Artifact Identification

Gawade, P., & Jadhav, B. D. (2026) · International Conference on Innovation & Growth in Next-Gen Intelligent Technology & Engineering (IGNITE) · doi:10.1109/ignite69592.2026.11705977

2013.Smart Technologies for Long-Term Stress Monitoring at Work, submitted to

Kocielnik, R., Eindhoven, T., Sidorova, N., Eindhoven, T., Maggi, F. M., Ouwerkerk, M., & Westerink, J. H. D. M. (2016)

A Survey on Sensor-Based Techniques for Continuous Stress Monitoring in Knowledge Work Environments

Kallio, J., Vildjiounaite, E., Tervonen, J., & López, M. B. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3720551 · Cited by 14

Towards the Stress Analytics Framework: Managing, Mining, and Visualizing Multi-modal Data for Stress Awareness

Kurniawan, H., & Pechenizkiy, M. (2014) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2014.129 · Cited by 2

Detection of Prolonged Stress in Smart Office

Vildjiounaite, E., Huotari, V., Kallio, J., Kyllönen, V., Mäkelä, S.-M., & Gimel’farb, G. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01177-2_90 · Cited by 7

Multimodal data collection framework for mental stress monitoring

Kye, S., Moon, J., Lee, J., Choi, I., Cheon, D., & Lee, K. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3125616 · Cited by 16