Activity-Aware Mental Stress Detection Using Physiological Sensors

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

A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

Lim, T.-S., Loh, W., & Shih, Y. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,130

Towards Real-Time Automatic Stress Detection for Office Workplaces

Suni-Lopez, F., Condorí-Fernández, N., & Catalá, A. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-11680-4_27 · Cited by 32

CASP: context-aware stress prediction system

Alharthi, R., Alharthi, R., Guthier, B., & Saddik, A. E. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5246-0 · Cited by 33

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm

Kim, H.-G., Song, S., Cho, B. H., & Jang, D. P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305864 · Cited by 29

Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks

Mina, T., Kannan, S. S., Jo, W., & Min, B. (2020) · IEEE Access · doi:10.1109/access.2020.3017659 · Cited by 32

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Sevil, M., Rashid, M. M., Askari, M. R., Maloney, Z., Hajizadeh, I., & Çınar, A. (2020) · Signals · doi:10.3390/signals1020011 · Cited by 31

Activity awareness can improve continuous stress detection in galvanic skin response

Tang, T. B., Wee, Y. L., & Lau, D. J. H. (2014) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2014.6985421 · Cited by 29

Neurostressology: A systematic review of EEG-based automated mental stress perspectives

Acharya, S., Khosravi, A., Creighton, D., Alizadehsani, R., & Acharya, U. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103368 · Cited by 29

A Survey on Mental Health Detection in Online Social Network

Rahman, R. A., Omar, K., Noah, S. A. M., & Danuri, M. S. N. M. (2018) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.8.4-2.6830 · Cited by 28

Towards multi-modal wearable driver monitoring: Impact of road condition on driver distraction

Dehzangi, O., & Williams, C. (2015) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2015.7299408 · Cited by 28

Stress management through regulation of blood pressure among college students

Joshi, A., Kiran, R., Singla, H. K., & Sah, A. N. (2016) · Work · doi:10.3233/wor-162308 · Cited by 28

MuSE: a Multimodal Dataset of Stressed Emotion

Jaiswal, M., Bara, C.-P., Luo, Y., Burzo, M. G., Mihalcea, R. F., & Provost, E. M. (2020) · Language Resources and Evaluation · Cited by 27

Machine and Deep Learning Algorithms for Wearable Health Monitoring

Fei, C., Liu, R., Li, Z., Wang, T., & Baig, F. N. (2021) · Health information science · doi:10.1007/978-3-030-68723-6_6 · Cited by 28

Effective stress detection using physiological parameters

Chauhan, M., Vora, S. V., & Dabhi, D. (2017) · International Conference on Innovations in Information, Embedded and Communication Systems · doi:10.1109/iciiecs.2017.8275853 · Cited by 27

Wearable Internet of Things for Personalized Healthcare: Study of Trends and Latent Research

Khan, S., & Alam, M. (2021) · Studies in computational intelligence · doi:10.1007/978-981-15-9735-0_3 · Cited by 26

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

Ahmadi, N., Sasangohar, F., Nisar, T., Danesh, V. C., Larsen, E. P., Sultana, I., & Bosetti, R. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211040889 · Cited by 29

On Interactive Data Visualization of Physiological Low-Cost-Sensor Data with Focus on Mental Stress

Holzinger, A., Bruschi, M., & Eder, W. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40511-2_34 · Cited by 26

Real-time personalized stress detection from physiological signals

Bin, M., Khalifa, O. O., & Saeed, R. A. (2015) · International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE) · doi:10.1109/iccneee.2015.7381390 · Cited by 25

StressAware: An app for real-time stress monitoring on the amulet wearable platform

Boateng, G., & Kotz, D. F. (2016) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc.2016.8284068 · Cited by 26

An Energy-Efficient Programmable Manycore Accelerator for Personalized Biomedical Applications

Kulkarni, A., Page, A., Attaran, N., Jafari, A., Malik, M., Homayoun, H., & Mohsenin, T. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2017.2754272 · Cited by 24

Towards a Non-Contact Method for Identifying Stress Using Remote Photoplethysmography in Academic Environments

Morales-Fajardo, H., Rodríguez-Arce, J., Gutiérrez-Cedeño, A., Viñas, J. C., Lagos, J. J. R., Abarca-Castro, E. A., Ramírez, C. I. L., & Vilchis-González, A. H. (2022) · Sensors · doi:10.3390/s22103780 · Cited by 26

Supportive hand-holding attenuates pupillary responses to stress in adult couples

Graff, T. C., Luke, S. G., & Birmingham, W. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212703 · Cited by 25

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Finseth, T., Dorneich, M. C., Vardeman, S. B., Keren, N., & Franke, W. D. (2023) · IEEE Access · doi:10.1109/access.2023.3254134 · Cited by 25

Effect of the Stroop test performed in supine position on the heart rate variability in both genders

Vazan, R., Filcikova, D., & Mravec, B. (2017) · Autonomic Neuroscience · doi:10.1016/j.autneu.2017.10.009 · Cited by 23

Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡

Pope, G., & Halter, R. J. (2019) · Sensors · doi:10.3390/s19112450 · Cited by 23

An Improved Approach for Stress Detection Using Physiological Signals

Nigam, K., Godani, K., Sharma, D., & Jain, S. (2021) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.14-5-2021.169919 · Cited by 22

Design of Unobtrusive Wearable Mental Stress Monitoring Device Using Physiological Sensor

Salafi, T., & Kah, J. C. Y. (2015) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-19452-3_4 · Cited by 22

Multi-modal interactive fusion method for detecting teenagers’ psychological stress

Zhang, H., Cao, L., Feng, L., & Yang, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103427 · Cited by 22

Affective Computing in Games

Guthier, B., Dörner, R., & Martínez, H. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46152-6_16 · Cited by 23

Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

Zhou, B., Ghose, T., & Lukowicz, P. (2020) · Sensors · doi:10.3390/s20030730 · Cited by 22

A multimodal Physiological-Based Stress Recognition: Deep Learning Models’ Evaluation in Gamers’ Monitoring Application

Dhaouadi, S., & Khelifa, M. M. B. (2020) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip49331.2020.9231666 · Cited by 20

Detection and Classification of Unannounced Physical Activities and Acute Psychological Stress Events for Interventions in Diabetes Treatment

Askari, M. R., Abdel-Latif, M. M., Rashid, M. M., Sevil, M., & Çınar, A. (2022) · Algorithms · doi:10.3390/a15100352 · Cited by 21

A regression approach based on separability maximization for modeling a continuous-valued stress index from electrocardiogram data

Ribeiro, R. T., & Cunha, J. P. S. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.06.006 · Cited by 20

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

Salleh, N. A., Malarvili, M. B., & Phillips, A. C. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01958-3 · Cited by 22

Experience

Boateng, G., Motti, V. G., Mishra, V., Batsis, J. A., Hester, J. D., & Kotz, D. F. (2019) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3300061.3345432 · Cited by 20

Stress Detection via Sensor Translation

Samyoun, S., Mondol, A. S., & Stankovic, J. A. (2020) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss49796.2020.00017 · Cited by 20

Towards Stress Detection in Real-Life Scenarios Using Wearable Sensors: Normalization Factor to Reduce Variability in Stress Physiology

Lamichhane, B., Großekathöfer, U., Schiavone, G., & Casale, P. (2016) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-49655-9_34 · Cited by 20

A Body Area Network for Ubiquitous Driver Stress Monitoring based on ECG Signal

Nita, S., Bitam, S., & Mellouk, A. (2019) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob.2019.8923468 · Cited by 20

A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team

Pandey, P., Lee, E. K., & Pompili, D. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2477342 · Cited by 19

Feature Selection and Machine Learning Based Multilevel Stress Detection from ECG Signals

Bichindaritz, I., Breen, C., Cole, E., Keshan, N., & Parimi, P. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-59397-5_22 · Cited by 21

Pervasive Healthcare-A Comprehensive Survey of Tools and TechniquesPreprint

Uniyal, D., & Raychoudhury, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.1821 · Cited by 19

Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being

Woodward, K., Kanjo, E., Brown, D. J., McGinnity, T. M., Inkster, B., Macintyre, D. J., & Tsanas, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3015018 · Cited by 18

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches

Amin, M., Ullah, K., Asif, M., Shah, H., Mehmood, A., & Khan, M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13111897 · Cited by 19

Stressing the dressing: Assessing stress during wound care in real-time using wearable sensors

Parvaneh, S., Grewal, G. S., Grewal, E., Menzies, R. A., Talal, T. K., Armstrong, D. G., STERNBERG, E. M., & Najafi, B. (2014) · Wound Medicine · doi:10.1016/j.wndm.2014.01.003 · Cited by 18

What to Put on the User

Hänsel, K., Poguntke, R., Haddadi, H., Alomainy, A., & Schmidt, A. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173719 · Cited by 19

Physiological Impact of Vibro-Acoustic Therapy on Stress and Emotions through Wearable Sensors

Delmastro, F., Martino, F. D., & Dolciotti, C. (2018) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2018.8480170 · Cited by 18

A Real-Time Cardiac Monitoring using a Multisensory Smart IoT System

Majumder, A. J. A., Elsaadany, M., Izaguirre, J. A., & Ucci, D. R. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.10220 · Cited by 18

Explainable Artificial Intelligence (XAI) Based Analysis of Stress Among Tech Workers Amidst COVID-19 Pandemic

Banerjee, J. S., Chakraborty, A., Mahmud, M., Kar, U., Lahby, M., & Saha, G. (2023) · Internet of things · doi:10.1007/978-3-031-28631-5_8 · Cited by 17

Survey of Sensor-Based Personal Wellness Management Systems

Yumak, Z., & Pu, P. (2013) · BioNanoScience · doi:10.1007/s12668-013-0099-0 · Cited by 18

Health monitoring laboratories by interfacing physiological sensors to mobile android devices

Rajan, D., Spanias, A., Ranganath, S., Banavar, M. K., & Spanias, P. (2013) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2013.6684987 · Cited by 17

Context-aware cardiac monitoring for early detection of heart diseases

Forkan, A. R. M., Khalil, I., & Tari, Z. (2013) · Swinburne figshare (Swinburne University of Technology) · Cited by 17

A Survey on Mobile Social Signal Processing

Palaghias, N., Hoseinitabatabaei, S. A., Nati, M., Gluhak, A., & Moessner, K. (2016) · ACM Computing Surveys · doi:10.1145/2893487 · Cited by 19

Human Factors Analysis Using Wearable Sensors in the Context of Cognitive and Emotional Arousal

Paletta, L., Pittino, N. M., Schwarz, M. H., Wagner-Hartl, V., & Kallus, K. W. (2015) · Procedia Manufacturing · doi:10.1016/j.promfg.2015.07.880 · Cited by 17

SMART WEARABLE STRESS MONITORING DEVICE FOR AUTISTIC CHILDREN

Airij, A. G., Bakhteri, R., & Khalil-Hani, M. (2016) · Jurnal Teknologi · doi:10.11113/jt.v78.9453 · Cited by 17

sEmoD: A Personalized Emotion Detection Using a Smart Holistic Embedded IoT System

Majumder, A. J. A., Mcwhorter, T. M., Ni, Y., Nie, H., Iarve, J., & Ucci, D. R. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00125 · Cited by 16

Effects of noise on arousal in a speech communication setting

Cvijanović, N., Kechichian, P., Janse, K., & Kohlrausch, A. (2017) · Speech Communication · doi:10.1016/j.specom.2017.02.001 · Cited by 17

Towards Unobtrusive Mental Well-Being Monitoring for Independent-Living Elderly

Huynh, S., Tan, H.-P., & Lee, Y. (2017) · doi:10.1145/3092305.3092311 · Cited by 16

A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals

Li, M., Xie, L., & Wang, Z. (2019) · Sensors · doi:10.3390/s19020429 · Cited by 16

Efficient classification model for anxiety detection in autism using intelligent search optimization based on Deep CNN

Umrani, A. T., & Harshavardhanan, P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17911-4 · Cited by 15

Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

Reimer, U., Laurenzi, E., Maier, E., & Ulmer, T. (2017) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2017.435 · Cited by 15

Intelligent health monitoring system modeling based on machine learning and agent technology

Elouni, J., Ellouzi, H., Ltifi, H., & Ayed, M. B. (2020) · Multiagent and Grid Systems · doi:10.3233/mgs-200329 · Cited by 15

Mobile real-time arousal detection

Alexandratos, V., Bulut, M., & Jasinschi, R. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854432 · Cited by 15

Pervasive Persuasion for Stress Self-Regulation

Wang, Y., Fischer, N., & Bry, F. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730850 · Cited by 15

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

Acevedo, C. M. D., Gómez, J. K. C., & Rojas, C. A. A. (2021) · Electronics · doi:10.3390/electronics10030301 · Cited by 15

Pervasive Healthcare Internet of Things: A Survey

Phung, K. A., Kirbas, C., Dereci, L., & Nguyen, T. (2022) · Information · doi:10.3390/info13080360 · Cited by 14

Feature Selection and Combination for Stress Identification Using Correlation and Diversity

Deng, Y., Hsu, D. F., Wu, Z., & Chu, C. (2012) · International Symposium on Pervasive Systems, Algorithms and Networks (ISPAN) · doi:10.1109/i-span.2012.12 · Cited by 14

Monitoring and Prediction of Mood in Elderly People during Daily Life Activities

Bautista‐Salinas, D., González, J. R., Méndez, I., & Mozos, Ó. M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857847 · Cited by 15

A Multimodal Approach to Improve the Robustness of Physiological Stress Prediction During Physical Activity

Parent, M., Tiwari, A., Albuquerque, I., Gagnon, J.-F., Lafond, D., Tremblay, S., & Falk, T. H. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914254 · Cited by 14

Participatory design of biomusic with users on the autism spectrum

Grond, F., Cascio, M. A., Motta‐Ochoa, R., Tembeck, T., Veen, D. T., & Blain‐Moraes, S. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925484 · Cited by 14

Design and validation of an electrophysiological based tool to assess chronic stress. Case study: burnout syndrome in caregivers

Mir, S. A., Pagès, E. G., López, B., Ribeiro, T. C., Rey, J. M. G., & Aguiló, J. (2020) · Stress · doi:10.1080/10253890.2020.1807512 · Cited by 16

Inducing and Assessing Acute Mental Stress in Controlled Conditions: Topical Review and Guidelines for Effective Experimental Protocols

Raggi, M., Moore, L. J., & Mesin, L. (2025) · IEEE Access · doi:10.1109/access.2025.3528518 · Cited by 14

Prioritizing data in emergency response based on context, message content and role

Luqman, F., Sun, F.-T., Cheng, H.-T., Buthpitiya, S., & Griss, M. (2011) · International Conference on Wireless Technologies for Humanitarian Relief · doi:10.1145/2185216.2185244 · Cited by 13

Secured ECG signal transmission for human emotional stress classification in wireless body area networks

Xu, H., & Hua, K. (2016) · EURASIP Journal on Information Security · doi:10.1186/s13635-015-0024-x · Cited by 13

Assessing the Effects of Stress Response on Glucose Variations

Sevil, M., Rashid, M. M., Hajizadeh, I., Maloney, Z., Samadi, S., Askari, M. R., Brandt, R., Hobbs, N., Park, M., Quinn, L., et al. (2019) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2019.8771094 · Cited by 14

Deep Learning Framework for Classification of Mental Stress from Multimodal Datasets

Shermadurai, P., & Thiyagarajan, K. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370119 · Cited by 12

Driver Lane Change Prediction Using Physiological Measures

Murphey, Y., Kochhar, D., Watta, P., Wang, X., & Wang, T. (2015) · SAE International Journal of Transportation Safety · doi:10.4271/2015-01-1403 · Cited by 11

Tools in NeuroIS Research: An Overview

Riedl‬, R., & Léger, P. (2015) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-662-45091-8_3 · Cited by 12

Mental Stress Evaluation of Car Driver in Different Road Complexity Using Heart Rate Variability (HRV) Analysis

Sugiono, S., Widhayanuriyawan, D., & Andriyani, D. P. (2018) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3309129.3309145 · Cited by 12

How do physiological responses such as respiratory frequency, heart rate, and galvanic skin response (GSR) change under emotional stress?

Civitello, D., Finn, D., Flood, M., Salievski, E., Schwarz, M., & Storck, Z. (2014) · Minds at UW (University of Wisconsin) · Cited by 12

Towards pervasive geospatial affect perception

Raja, M., Exler, A., Hemminki, S., Konomi, S., Sigg, S., & Inoue, S. (2017) · GeoInformatica · doi:10.1007/s10707-017-0294-1 · Cited by 12

Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning

Decker, J., Boborzi, L., Schniepp, R., Jahn, K., & Wuehr, M. (2024) · Sensors · doi:10.3390/s24196442 · Cited by 11

Hybrid Artificial Intelligence and IoT in Health care for Cardiovascular Patient in Decision-Making System

Safa, M., Pandian, A., Kartick, T., Chakrapani, K., Geetha, G., & Saranya, G. (2021) · Intelligent systems reference library · doi:10.1007/978-981-16-2972-3_7 · Cited by 11

Measuring Stress in an Augmented Training Environment: Approaches and Applications

Jones, D., & Dechmerowski, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39952-2_3 · Cited by 11

Feasibility study of practical vital sign detection using millimeter-wave radios

Wang, W., Jia, Z., Xu, C., Luo, G., Zhang, D., An, N., & Zhang, Y. (2021) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-021-00080-4 · Cited by 11

Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters

Hörmann, T., Hesse, M., Christ, P., Adams, M., Menßen, C., & Rückert, U. (2016) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0005665000420051 · Cited by 10

Stress Assessment for Augmented Reality Applications Based on Head Movement Features

Ferrarotti, A., Baldoni, S., Carli, M., & Battisti, F. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3385637 · Cited by 10

Analysis of Teens’ Chronic Stress on Micro-blog

Xue, Y., Li, Q., Zhao, L., Jia, J., Feng, L., Yu, F., & Clifton, D. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48743-4_10 · Cited by 10

Cooperative Human-Machine Interaction in Industrial Environments

Antão, L., Rui, P.-T., Reis, J., Gonçalves, G., & Pereira, F. L. L. (2018) · 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO) · doi:10.1109/controlo.2018.8514549 · Cited by 11

Measuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise

Zhang, L., King, Z., Egilmez, B., Reeder, J. T., Ghaffari, R., Rogers, J. A., Rosen, K. D., Bass, M., Moskowitz, J. T., Tandon, S. D., et al. (2018) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2018.8329683 · Cited by 10

Leveraging ECG signals and social media for stress detection

Feng, Z., Li, N., Feng, L., Chen, D., & Zhu, C. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1673820 · Cited by 10

Physiological changes over the course of cognitive bias modification for social anxiety

Boukhechba, M., Gong, J., Kowsari, K., Ameko, M. K., Fua, K., Chow, P. I., Huang, Y., Teachman, B. A., & Barnes, L. E. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333458 · Cited by 9

Enhancing Stress Understanding Through Team Reflection: Technology-Driven Insights in High-Stress Training Scenarios

Akiri, S., Misal, V., Taherzadeh, S., Jenkins, J. L., Williams, G., Mentis, H. M., & Kleinsmith, A. (2024) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3663384.3663385 · Cited by 10

Probabilistic segmentation of time-series audio signals using Support Vector Machines

Kalantarian, H., Mortazavi, B. J., Pourhomayoun, M., Alshurafa, N. I., & Sarrafzadeh, M. (2016) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2016.04.011 · Cited by 9

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Pope, G., Mishra, V., Lewia, S., Lowens, B. M., Kotz, D. F., Lord, S., & Halter, R. J. (2018) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2018.8329691 · Cited by 9

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Gempton, N., Skalistis, S., Furness, J., Shaikh, S. A., & Petrov́ić, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39354-9_28 · Cited by 9

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Okkonen, J., Heimonen, T., Savolainen, R., & Turunen, M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-60492-3_41 · Cited by 12

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Ahmad, W. N. W., Adib, M. A. H. M., Manap, Z. A., Zaihidee, F. M., & Txi, M. R. S. (2022) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2022-si5(2)-11 · Cited by 11

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Lu, P., Zhang, W., Liang Ma, A., & Zhao, Q. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49913-6_14 · Cited by 8

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