Anxiety level recognition for virtual reality therapy system using physiological signals
Open accessAbstract
Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.
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Real-time measurement of physiological data during VRET can be used to automatically classify the level of anxiety through blood pressure or galvanic skin response.
“Real-time measurement of physiological data acquired during VRET can even be used to automatically classify the level of anxiety through blood pressure or galvanic skin response (Šalkevicius et al., 2019).”
From Virtual Reality exposure therapy in the treatment of public speaking anxiety and social anxiety disorder (2024) · cites this paper as Šalkevicius et al., 2019
The Psychological Signal Recording section says GSR and BVP were collected during VRET, and the Discussion and Conclusions reports a four-level anxiety classification model and the possibility of real-time recognition. But the paper measures blood volume pulse (BVP), not blood pressure, and describes integration of the model into the VRET interface as future work rather than demonstrating deployed real-time classification. The provided copy gives no page numbers for these sections. (LLM, conf=0.87)
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. P. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 10,445 · see citing works
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093 · see citing works
The practice of behavior therapy
Wolpe, J. (1969) · Cited by 2,343 · see citing works
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 · see citing works
Epidemiology of anxiety disorders in the 21st century
Bandelow, B., & Michaelis, S. (2015) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2015.17.3/bbandelow · Cited by 2,079 · see citing works
Katsigiannis, S., & Ramzan, N. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2688239 · Cited by 1,135 · see citing works
Carl, E., Stein, A. T., Levihn-Coon, A., Pogue, J. R., Rothbaum, B., Emmelkamp, P., Asmundson, G. J. G., Carlbring, P., & Powers, M. B. (2019) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2018.08.003 · Cited by 889 · see citing works
Carpenter, J. K., Andrews, L. A., Witcraft, S. M., Powers, M. B., Smits, J. A. J., & Hofmann, S. G. (2018) · Depression and Anxiety · doi:10.1002/da.22728 · Cited by 846 · cited 4× · see citing works
“Anxiety disorders can be treated using cognitive behavioral therapy (CBT) [5].”
BackgroundInfluentialDedovic, 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 731 · see citing works
Stress detection in daily life scenarios using smart phones and wearable sensors: A survey
Can, Y. S., Arnrich, B., & Ersoy, C. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103139 · Cited by 439 · see citing works
Freeman, D., Haselton, P., Freeman, J., Spanlang, B., Kishore, S., Albery, E., Denne, M., Brown, P., Slater, M., & Nickless, A. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30226-8 · Cited by 377 · see citing works
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses
Abadi, M. K., Subramanian, R., Kia, S. M., Avesani, P., Patras, I., & Sebe, N. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2392932 · Cited by 367 · see citing works
Advances in Photopletysmography Signal Analysis for Biomedical Applications
Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., De Albuquerque, V. H. C., & Alexandria, A. R. (2018) · Sensors · doi:10.3390/s18061894 · Cited by 292 · see citing works
Emotion Based Music Recommendation System Using Wearable Physiological Sensors
Ayata, D., Yaslan, Y., & Kamaşak, M. E. (2018) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2018.2844736 · Cited by 279 · see citing works
Chen, L., Zhao, Y., Ye, P., Zhang, J., & Zou, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.040 · Cited by 243 · see citing works
Continuous stress detection using a wrist device
Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968306 · Cited by 240 · see citing works
Stress detection from speech and Galvanic Skin Response signals
Kurniawan, H., Maslov, A. V., & Pechenizkiy, M. (2013) · Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627790 · Cited by 227 · see citing works
A survey of machine learning techniques in physiology based mental stress detection systems
Panicker, S., & Gayathri, P. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.01.004 · Cited by 217 · see citing works
Garbarino, M., Lai, M., Tognetti, S., Picard, R. W., & Bender, D. (2014) · Wireless Mobile Communication and Healthcare · doi:10.4108/icst.mobihealth.2014.257418 · Cited by 215 · see citing works
Consumer grade EEG devices: are they usable for control tasks?
Maskeliūnas, R., Damaševičius, R., Martišius, I., & Vasiljevas, M. (2016) · PeerJ · doi:10.7717/peerj.1746 · Cited by 211 · see citing works
Beidel, D. C., Frueh, B. C., Neer, S. M., Bowers, C. A., Trachik, B., Uhde, T. W., & Grubaugh, A. (2019) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2017.08.005 · Cited by 195 · see citing works
Cluster-Based Analysis for Personalized Stress Evaluation Using Physiological Signals
Xu, Q., Nwe, T. L., & Guan, C. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2311044 · Cited by 165 · see citing works
Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals
Wen, W., Liu, G., Cheng, N., Wei, J., Shangguan, P., & Huang, W. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2327617 · Cited by 150 · see citing works
Keep the Stress Away with SoDA: Stress Detection and Alleviation System
Akmandor, A. O., & Jha, N. K. (2017) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2017.2703613 · Cited by 142 · see citing works
A meta-analytic examination of attrition in virtual reality exposure therapy for anxiety disorders
Benbow, A. A., & Anderson, P. L. (2019) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2018.06.006 · Cited by 136 · cited 2× · see citing works
“Moreover, a recent survey of VRET studies showed that it can be equally effective as in vivo exposure therapy and even has a slightly lower dropout rate compared to traditional treatment [11].”
BackgroundResultStress Detection Using Wearable Physiological Sensors
Săndulescu, V., Andrews, S., Ellis, D. A., Bellotto, N., & Mozos, Ó. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18914-7_55 · Cited by 135 · see citing works
Diemer, J., Lohkamp, N., Mühlberger, A., & Zwanzger, P. (2015) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2015.10.007 · Cited by 116 · see citing works
Machine learning-based signal processing using physiological signals for stress detection
Ghaderi, A., Frounchi, J., & Farnam, A. (2015) · Iranian Conference on Biomedical Engineering · doi:10.1109/icbme.2015.7404123 · Cited by 107 · see citing works
Norrholm, S. D., Jovanović, T., Gerardi, M. A., Breazeale, K. G., Price, M., Davis, M., Duncan, E., Ressler, K. J., Bradley, B., Rizzo, A., et al. (2016) · Behaviour Research and Therapy · doi:10.1016/j.brat.2016.05.002 · Cited by 96 · see citing works
Gujjar, K. R., van Wijk, A. J., Abdullah, F., Islam, M. N., Bernatchez, M., & de Jongh, A. (2016) · BMC Oral Health · doi:10.1186/s12903-016-0186-z · Cited by 88 · see citing works
Krishna, N. M., Sekaran, K., Vamsi, A. V. N., Ghantasala, G. S. P., Chandana, P., Kadry, S., Blažauskas, T., & Damaševičius, R. (2019) · IEEE Access · doi:10.1109/access.2019.2922047 · Cited by 84 · see citing works
Real time stress detection system based on EEG signals
Vanitha, V., & Krishnan, P. (2016) · Biomedical Research-tokyo · Cited by 79 · see citing works
van Gent, P., Farah, H., van Nes, N., & van Arem, B. (2018) · Research Repository (Delft University of Technology) · Cited by 75 · see citing works
Le, Q. A., Doctor, J. N., Zoellner, L. A., & Feeny, N. C. (2014) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.13m08719 · Cited by 69 · see citing works
From Extinction Learning to Anxiety Treatment: Mind the Gap
Carpenter, J. K., Pinaire, M., & Hofmann, S. G. (2019) · Brain Sciences · doi:10.3390/brainsci9070164 · Cited by 69 · see citing works
BioSPPy - Biosignal Processing in Python
Carreiras, C., Alves, A. P., Lourenço, A., Canento, F., Silva, H., & Fred, A. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.11048614 · Cited by 66 · see citing works
Evidence-Based Assessment and Treatment of Specific Phobias in Adults
Hood, H. K., & Antony, M. M. (2012) · Autism and child psychopathology series · doi:10.1007/978-1-4614-3253-1_2 · Cited by 46 · see citing works
Insightful stress detection from physiology modalities using Learning Vector Quantization
de Vries, G.-J., Pauws, S. C., & Biehl, M. L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.10.008 · Cited by 41 · see citing works
Castaldo, R., Montesinos, L., Melillo, P., Massaro, S., & Pecchia, L. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-5122-7_161 · Cited by 31 · see citing works
How Major Depressive Disorder Affects the Ability to Decode Multimodal Dynamic Emotional Stimuli
Scibelli, F., Troncone, A., Likforman-Sulem, L., Vinciarelli, A., & Esposito, A. A. (2016) · Frontiers in ICT · doi:10.3389/fict.2016.00016 · Cited by 24 · see citing works
Mertens, G., Wagensveld, P., & Engelhard, I. M. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.10.006 · Cited by 23 · see citing works
Cloud Based Virtual Reality Exposure Therapy Service for Public Speaking Anxiety
Šalkevičius, J., Miškinytė, A., & Navickas, L. (2019) · Information · doi:10.3390/info10020062 · Cited by 21 · see citing works
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 · see citing works
Cloudification of Virtual Reality Gliding Simulation Game
Buzys, R., Maskeliūnas, R., Damaševičius, R., Sidekerskienė, T., Woźniak, M., & Wei, W. (2018) · Information · doi:10.3390/info9120293 · Cited by 17 · see citing works
Recognition of public speaking anxiety on the recurrence quantification analysis of GSR signals
Zhang, X., Wen, W., Liu, G., & Hu, H. (2016) · International Conference on Information Science and Technology · doi:10.1109/icist.2016.7483472 · Cited by 16 · see citing works
Gender, Age, Colour, Position and Stress: How They Influence Attention at Workplace?
Raudonis, V., Maskeliūnas, R., Stankevičius, K., & Damaševičius, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62404-4_19 · Cited by 16 · see citing works
Speaker-independent speech emotion recognition based on random forest feature selection algorithm
Cao, W., Xu, J., & Liu, Z. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8029112 · Cited by 16 · see citing works
Depression Behavior Detection Model Based on Participation in Serious Games
Maskeliūnas, R., Blažauskas, T., & Damaševičius, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60840-2_31 · Cited by 13 · see citing works
Identification of Human Response to Virtual 3D Face Stimuli
Vaškevičius, E., Vidugirienė, A., & Kaminskas, V. (2014) · Information Technology And Control · doi:10.5755/j01.itc.43.1.5927 · Cited by 7 · see citing works
Šalkevičius, J., & Navickas, L. (2018) · International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) · doi:10.1109/w-ficloud.2018.00040 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Virtual reality for supporting the treatment of depression and anxiety: scoping review
Baghaei, N., Chitale, V., Hlasnik, A., Stemmet, L., Liang, H.-N., & Porter, R. (2021) · JMIR Mental Health · doi:10.2196/29681 · Cited by 197 · see citing works
Halbig, A., & Latoschik, M. E. (2021) · Frontiers in Virtual Reality · doi:10.3389/frvir.2021.694567 · Cited by 166 · see citing works
Virtual reality therapy in social anxiety disorder
Emmelkamp, P. M. G., Meyerbröker, K., & Morina, N. (2020) · Current Psychiatry Reports · doi:10.1007/s11920-020-01156-1 · Cited by 150 · see citing works
Bulagang, A. F., Ng, G. W., Mountstephens, J., & Teo, J. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100363 · Cited by 114 · see citing works
Opportunities and Challenges of Virtual Reality in Healthcare – A Domain Experts Inquiry
Halbig, A., Babu, S. K., Gatter, S., Latoschik, M. E., Brukamp, K., & von Mammen, S. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.837616 · Cited by 111 · see citing works
Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review
Abd-Alrazaq, A. A., AlSaad, R., Aziz, S., Ahmed, A., Denecke, K., Househ, M. S., Farooq, F., & Sheikh, J. I. (2022) · Journal of Medical Internet Research · doi:10.2196/42672 · Cited by 113 · see citing works
A machine-learning approach for stress detection using wearable sensors in free-living environments
Al-Alim, M. A., Mubarak, R. I., Salem, N. M., & Sadek, I. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108918 · Cited by 85 · see citing works
Balan, O. C., Moise, G., Moldoveanu, A. D. B., Leordeanu, M., & Moldoveanu, F. (2020) · Sensors · doi:10.3390/s20020496 · Cited by 82 · see citing works
Razavi, M., Ziyadidegan, S., Mahmoudzadeh, A., Kazeminasab, S., Baharlouei, E., Janfaza, V., Jahromi, R. K., & Sasangohar, F. (2024) · JMIR Mental Health · doi:10.2196/53714 · Cited by 76 · see citing works
Lee, J., Lee, H., & Shin, M. (2021) · Sensors · doi:10.3390/s21072381 · Cited by 63 · see citing works
BioSPPy: A Python toolbox for physiological signal processing
Bota, P. J., Silva, R., Carreiras, C., Fred, A. L. N., & Silva, H. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101712 · Cited by 63 · see citing works
Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection
Petrescu, L., Petrescu, C., Mitruț, O., Moise, G., Moldoveanu, A. D. B., Moldoveanu, F., & Leordeanu, M. (2020) · Sensors · doi:10.3390/s20247088 · Cited by 62 · see citing works
Paulauskas, L., Paulauskas, A., Blažauskas, T., Damaševičius, R., & Maskeliūnas, R. (2023) · Technologies · doi:10.3390/technologies11020036 · Cited by 57 · see citing works
Human state anxiety classification framework using EEG signals in response to exposure therapy
Muhammad, F., & Al-Ahmadi, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265679 · Cited by 52 · see citing works
Assessing Facial Symmetry and Attractiveness using Augmented Reality
Wei, W., Ho, E. S. L., McCay, K. D., Damaševičius, R., Maskeliūnas, R., & Esposito, A. A. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00975-z · Cited by 53 · see citing works
SoK: Data Privacy in Virtual Reality
Garrido, G. M., Nair, V. C., & Song, D. X. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2024-0003 · Cited by 68 · see citing works
An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress
Baird, A., Triantafyllopoulos, A., Zänkert, S., Ottl, S., Christ, L., Stappen, L., Konzok, J., Sturmbauer, S. C., Meßner, E.-M., Kudielka, B. M., et al. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.750284 · Cited by 47 · see citing works
A meta-analysis of the effect of virtual reality on reducing public speaking anxiety
Lim, M. H., Aryadoust, V., & Esposito, G. (2022) · Current Psychology · doi:10.1007/s12144-021-02684-6 · Cited by 45 · see citing works
Moinnereau, M.-A., de Oliveira, A. A., & Falk, T. H. (2022) · Quality and User Experience · doi:10.1007/s41233-022-00052-1 · Cited by 45 · see citing works
Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis
Abd-Alrazaq, A. A., AlSaad, R., Harfouche, M., Aziz, S., Ahmed, A., Damseh, R., & Sheikh, J. I. (2023) · Journal of Medical Internet Research · doi:10.2196/48754 · Cited by 48 · see citing works
Suzuki, K., Laohakangvalvit, T., Matsubara, R., & Sugaya, M. (2021) · Sensors · doi:10.3390/s21092910 · Cited by 44 · see citing works
Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device
Mai, N.-D., Lee, B. G., & Chung, W. (2021) · Sensors · doi:10.3390/s21155135 · Cited by 37 · see citing works
Li, Y., Li, K., Wang, S., Chen, X., & Wen, D. (2022) · Biosensors · doi:10.3390/bios12060404 · Cited by 37 · see citing works
Multimodal Classification of Anxiety Based on Physiological Signals
Vaz, M., Summavielle, T., Sebastião, R., & Ribeiro, R. P. (2023) · Applied Sciences · doi:10.3390/app13116368 · Cited by 37 · see citing works
Henry, J., Lloyd, H., Turner, M. J., & Kendrick, C. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3276413 · Cited by 32 · see citing works
Stress recognition with multi‐modal sensing using bootstrapped ensemble deep learning model
Singh, G., Phukan, O. C., & Kumar, R. (2023) · Expert Systems · doi:10.1111/exsy.13239 · Cited by 30 · see citing works
Seuling, P. D., Czernin, N. S., & Schiele, M. A. (2024) · Neuroscience Applied · doi:10.1016/j.nsa.2024.104074 · Cited by 28 · see citing works
“Real-time measurement of physiological data acquired during VRET can even be used to automatically classify the level of anxiety through blood pressure or galvanic skin response ( ˇ et al., 2019).”
Takac, M., Collett, J., Conduit, R., & Foe, A. D. (2021) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.2624 · Cited by 28 · see citing works
Tao, K., Huang, Y., Shen, Y., & Sun, L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3192571 · Cited by 28 · see citing works
Chitale, V., Baghaei, N., Playne, D. P., Liang, H., Zhao, Y., Erensoy, A., & Ahmad, Y. (2022) · Games for Health Journal · doi:10.1089/g4h.2021.0227 · Cited by 28 · see citing works
Bharathi, V. M., Manimegalai, P., George, S. T., Pamela, D., Mohammed, M. A., Abdulkareem, K. H., Jaber, M. M., & Damaševičius, R. (2024) · Virtual Reality · doi:10.1007/s10055-024-01065-1 · Cited by 27 · see citing works
Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device
Chen, S., Jiang, K., Hu, H. R., Kuang, H., Yang, J., Luo, J., Chen, X., & Li, Y. (2021) · Sensors · doi:10.3390/s21031018 · Cited by 28 · see citing works
A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment
Senaratne, H., Oviatt, S. L., Ellis, K., & Melvin, G. A. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3556980 · Cited by 28 · see citing works
Respiratory Markers Significantly Enhance Anxiety Detection Using Multimodal Physiological Sensing
Gazi, A. H., Lis, P., Mohseni, A., Ompi, C., Giuste, F., Shi, W., Inan, O. T., & Wang, M. D. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508589 · Cited by 25 · see citing works
Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications
Orozco-Mora, C. E., Oceguera‐Cuevas, D., Fuentes-Aguilar, R. Q., & Hernández-Melgarejo, G. (2022) · IEEE Access · doi:10.1109/access.2022.3186318 · Cited by 25 · see citing works
Masuda, N., & Yairi, I. E. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1141801 · Cited by 24 · see citing works
Park, J.-H., Shin, Y.-B., Jung, D., Hur, J., Pack, S. P., Lee, H.-J., Lee, H., & Cho, C. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1504190 · Cited by 32 · see citing works
Bolinski, F., Etzelmüller, A., De Witte, N. A. J., van Beurden, C., Debard, G., Bonroy, B., Cuijpers, P., Riper, H., & Kleiboer, A. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.103877 · Cited by 23 · see citing works
Mevlevioğlu, D., Tabirca, S., & Murphy, D. (2024) · Biosensors · doi:10.3390/bios14030131 · Cited by 23 · see citing works
Dynamic Tracking of State Anxiety via Multi-Modal Data and Machine Learning
Ding, Y., Liu, J., Zhang, X., & Yang, Z. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.757961 · Cited by 22 · see citing works
Maskeliūnas, R., Damaševičius, R., Paulauskas, A., Ceravolo, M. G., Charalambous, M., Kambanaros, M., Pampoulou, E., Barbabella, F., Poli, A., & de Carvalho, C. V. (2022) · Information · doi:10.3390/info13120564 · Cited by 23 · see citing works
Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines
Shahbazi, Z., & Byun, Y.-C. (2023) · Biology · doi:10.3390/biology12010091 · Cited by 22 · see citing works
SensCon: Embedding Physiological Sensing into Virtual Reality Controllers
Chiossi, F., Kosch, T., Menghini, L., Villa, S., & Mayer, S. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3604270 · Cited by 21 · see citing works
A Multimodal Data-Driven Framework for Anxiety Screening
Mo, H., Hui, S. C., Liao, X., Li, Y., Zhang, W., & Ding, S. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3352713 · Cited by 20 · see citing works
A Machine Learning Approach to Automatic Phobia Therapy with Virtual Reality
Balan, O. C., Moldoveanu, A. D. B., & Leordeanu, M. (2021) · Contemporary clinical neuroscience · doi:10.1007/978-3-030-54564-2_27 · Cited by 20 · see citing works
Lee, G., & Lee, S. H. (2022) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002341 · Cited by 20 · see citing works
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 23 · see citing works
Virtual Reality Utilized for Safety Skills Training for Autistic Individuals: A Review
Liu, L., Yao, X., Chen, J., Zhang, K., Liu, L., Wang, G., & Ling, Y. (2024) · Behavioral Sciences · doi:10.3390/bs14020082 · Cited by 20 · see citing works
Kańtoch, E., & Kańtoch, A. (2020) · Sensors · doi:10.3390/s20185181 · Cited by 19 · see citing works
The future of prejudice reduction research: A critical review of the role of virtual reality (VR)
Chen, R., & White, F. A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108073 · Cited by 19 · see citing works
Figueroa, C., Ayala, A., Trejo, L. A., Ramos, B., Briz, C. L., Noriega, I., & Chávez, A. (2023) · Sensors · doi:10.3390/s23052650 · Cited by 19 · see citing works
Use of Virtual Reality to Reduce Anxiety and Pain of Adults Undergoing Outpatient Procedures
Brown, K., & Foronda, C. L. (2020) · Informatics · doi:10.3390/informatics7030036 · Cited by 21 · see citing works
Amin, M., Ullah, K., Asif, M., Shah, H., Mehmood, A., & Khan, M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13111897 · Cited by 18 · see citing works
Ørskov, P. T., Lichtenstein, M. B., Ernst, M. T., Fasterholdt, I., Matthiesen, A. F., Scirea, M., Bouchard, S., & Andersen, T. E. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.991755 · Cited by 18 · see citing works
Multi-modal physiological signals based fear of heights analysis in virtual reality scenes
Zheng, R., Wang, T., Cao, J., Vidal, P., & Wang, D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102988 · Cited by 17 · see citing works
Gutiérrez-Martín, L., Romero, E., Andújar, C. S. D. B., Canabal, M. F., Rodríguez-Ramos, G. E., Toro-Flores, R., López‐Ongil, S., & López-Ongil, C. (2022) · Sensors · doi:10.3390/s22114023 · Cited by 17 · see citing works
Prajapati, V., Guha, R., & Routray, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254335 · Cited by 15 · see citing works
Christian, C., Cash, E. D., Cohen, D. A., Trombley, C. M., & Levinson, C. A. (2022) · Clinical Psychological Science · doi:10.1177/21677026221083284 · Cited by 16 · see citing works
Diagnosis of Cardiovascular Diseases by Ensemble Optimization Deep Learning Techniques
Oyewola, D. O., Dada, E. G., & Misra, S. (2023) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.334021 · Cited by 15 · see citing works
Sensor fusion-based virtual reality for enhanced physical training
Li, X., Fan, D., Deng, Y. J., Lei, Y., & Omalley, O. (2024) · Robotic Intelligence and Automation · doi:10.1108/ria-08-2023-0103 · Cited by 20 · see citing works
Todorov, A. R., Torah, R. N., Wagih, M., Ardern‐Jones, M. R., & Beeby, S. (2023) · Sensors · doi:10.3390/s23083935 · Cited by 15 · see citing works
Zainab, H. E., Bawany, N. Z., Rehman, W., & Imran, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15756-5 · Cited by 13 · see citing works
Automatic motion artifact detection in electrodermal activity signals using 1D U-net architecture
Kong, Y., Hossain, M.-B., Peitzsch, A. G., Posada-Quintero, H. F., & Chon, K. H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109139 · Cited by 15 · see citing works
Kim, J., & Lee, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241498 · Cited by 13 · see citing works
Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals
Scheutz, M. J., Aeron, S., Aygun, A., de Ruiter, J. P., Fantini, S., Fernandez, C. M., Haga, Z., Nguyen, T. D., & Lyu, B. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12669 · Cited by 13 · see citing works
Emotional Analysis of Drug Addicted Patients using Virtual Reality Therapy and EEG Signals
Kataria, A., Singh, G., & Sandhu, J. K. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307713 · Cited by 12 · see citing works
Evaluation of Galvanic Skin Response (GSR) Signals Features for Emotion Recognition
Kipli, K., Latip, A. A. A., Lias, K., Bateni, N., Yusoff, S. M., Suud, J., Jalil, M. A. B., Ray, K., Kaiser, M. S., & Mahmud, M. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24801-6_19 · Cited by 12 · see citing works
Moon, E., Yang, M. L., Seon, Q., & Mantere, O. (2021) · Current Psychiatry Reports · doi:10.1007/s11920-021-01291-3 · Cited by 13 · see citing works
Lee, J., Kim, C. G., & Lee, K. C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.705442 · Cited by 13 · see citing works
ConVRgence (VRIC) Virtual Reality International Conference Proceedings
Richir, S. (2020) · International Journal of Virtual Reality · doi:10.20870/ijvr.2020...3316 · Cited by 11 · see citing works
An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures
de-Juan-Ripoll, C., Llanes-Jurado, J., Giglioli, I. A. C., Marín‐Morales, J., & Alcañíz, M. (2021) · Applied Sciences · doi:10.3390/app11020825 · Cited by 11 · see citing works
Shikha, S., Sethia, D., & Indu, S. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adb006 · Cited by 10 · see citing works
de With, L. A., Thammasan, N., & Poel, M. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.652550 · Cited by 10 · see citing works
Tseng, C.-H., Lin, H.-C. K., Huang, A. C. W., & Lin, J.-R. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2245637 · Cited by 10 · see citing works
Affective Computing for eHealth Using Low-Cost Remote Internet of Things-Based EMG Platform
Tamulis, Ž., Vasiljevas, M., Damaševičius, R., Maskeliūnas, R., & Misra, S. (2022) · Internet of things · doi:10.1007/978-3-030-81473-1_3 · Cited by 10 · see citing works
Automatic artifact recognition and correction for electrodermal activity based on LSTM-CNN models
Llanes-Jurado, J., Carrasco-Ribelles, L. A., Alcañíz, M., Soria‐Olivas, E., & Marín‐Morales, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120581 · Cited by 16 · see citing works
Stress Detection Using Physiological Signals Based On Machine Learning
Hota, A., & Park, S.-W. (2022) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci58124.2022.00074 · Cited by 9 · see citing works
Zhang, H., Li, X., Sun, Y., Fu, X., Qiu, C., & Carroll, J. M. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr58804.2024.00054 · Cited by 10 · see citing works
SFF-DA: Spatiotemporal Feature Fusion for Nonintrusively Detecting Anxiety
Mo, H., Li, Y., Han, P., Liao, X., Zhang, W., & Ding, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3341132 · Cited by 8 · see citing works
Wearable devices for anxiety assessment: a systematic review
Elgendi, M., Markov, K., Liu, H., Vos, M. D., Khalaf, K. A., Khandoker, A. H., Jelinek, H. F., Goswami, D., & Menon, C. (2026) · Communications Medicine · doi:10.1038/s43856-025-01234-6 · Cited by 9 · see citing works
Rezaee, K., & Zolfaghari, S. (2021) · Computational Intelligence · doi:10.1111/coin.12480 · Cited by 8 · see citing works
Maskeliūnas, R., Damaševičius, R., Kulikajevas, A., Marley, J., & Larsson, C. (2022) · Informatics · doi:10.3390/informatics9020040 · Cited by 8 · see citing works
Application of Immersive VR Serious Games in the Treatment of Schizophrenia Negative Symptoms
Miranda, B., Rego, P. A., Romero, L., & Moreira, P. M. (2024) · Computers · doi:10.3390/computers13020042 · Cited by 8 · see citing works
Dastan, M., Ricci, M., Vangi, F., & Fiorentino, M. (2024) · Sensors · doi:10.3390/s24061732 · Cited by 8 · see citing works
Cheng, X., Bao, B., Cui, W., Liu, S., Zhong, J., Cai, L., & Yang, H. (2023) · Sensors · doi:10.3390/s23125482 · Cited by 7 · see citing works
A systematic review on physiology-based anxiety detection using machine learning
Shikha, S., Sethia, D., & Indu, S. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/add5fc · Cited by 7 · see citing works
Yan, D., & Guo, Q. (2023) · Buildings · doi:10.3390/buildings13082086 · Cited by 7 · see citing works
Machine Learning based Anxiety Detection using Physiological Signals and Context Features
Jain, A., & Kumar, R. (2024) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct61598.2024.10551003 · Cited by 7 · see citing works
Deep transfer learning in human–robot interaction for cognitive and physical rehabilitation purposes
Ilyas, C. M. A., Rehm, M., Nasrollahi, K., Madadi, Y., Moeslund, T. B., & Seydi, V. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00988-8 · Cited by 9 · see citing works
Physiological Sensing and Machine Learning Approaches Toward Anxiety Detection: A Systematic Review
Ahmadi, N., Abbasian, P., Hou, Q., Powell, L., & Hammond, T. (2025) · IEEE Sensors Reviews · doi:10.1109/sr.2025.3643386 · Cited by 7 · see citing works
MGP: a monitoring-based VR interactive mode to support guided practice
Wang, Y., Luan, H., Wang, L., Song, S., Bian, Y., Bao, X., Liu, R., Gai, W., Lv, G., & Yang, C. (2023) · The Visual Computer · doi:10.1007/s00371-023-02950-7 · Cited by 6 · see citing works
Real-time anxiety prediction in virtual reality therapy
Mevlevioğlu, D., Tabirca, S., & Murphy, D. (2022) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3524273.3533926 · Cited by 6 · see citing works
J, A. S. P., & Sherly, K. K. (2023) · International Conference on Computational Systems and Communication (ICCSC) · doi:10.1109/iccsc56913.2023.10143026 · Cited by 6 · see citing works
Llanes-Jurado, J., Carrasco-Ribelles, L. A., Alcañíz, M., & Marín‐Morales, J. (2021) · Research Square · doi:10.21203/rs.3.rs-717360/v1 · Cited by 6 · see citing works
Biomarkers in exposure-based treatment of anxiety in virtual reality: a systematic review
Ernst, M. T., Nielsen, J. H., Runge, E., Bouchard, S., & Clemmensen, L. (2024) · Frontiers in Virtual Reality · doi:10.3389/frvir.2024.1355082 · Cited by 5 · see citing works
Anxiety classification in virtual reality using biosensors: A mini scoping review
Mevlevioğlu, D., Tabirca, S., & Murphy, D. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287984 · Cited by 5 · see citing works
ATHENA I—An architecture for near real-time physiological signal monitoring and pattern detection
Fröhlich, W. D. R., Rigo, S. J., & Bez, M. R. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.010 · Cited by 5 · see citing works
Fradette, M.-J., Azrak, J., Cousineau, F., Désilets, M., & Dumais, A. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030048 · Cited by 4 · see citing works
Hosseini-Toudeshky, H., Seidnitzer, S., Bickelhaupt, S., Harmouche, R., & Kersten‐Oertel, M. (2025) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr59515.2025.00084 · Cited by 4 · see citing works
Research on fear mental resilience training based on virtual reality and dynamic decision fusion
Yu, Y., He, B., Yu, G., & Zhong, F. (2023) · doi:10.1109/prmvia58252.2023.00045 · Cited by 4 · see citing works
- Authors
- Šalkevicius, J., Damaševičius, R., Maskeliunas, R., & Laukienė, I.
- Affiliations
- Department of Software Engineering, Kaunas University of Technology, Kaunas 51368, Lithuania
- Silesian University of Technology (PL)
- Institute of Mathematics, Silesian University of Technology, Gliwice 44-100, Poland
- Lithuanian University of Health Sciences (LT)
- Corresponding author
- Damaševičius, Robertas
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2019-09-16
- Volume
- 8
- Issue
- 9
- Pages
- 1039
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W2974802536
- Cited by
- 135
- References
- 53
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 24 · 2023: 33 · 2022: 26 · 2021: 22 · 2020: 8
- FWCI
- 16.48
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- KTUePubl (Repository of Kaunas University of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lithuanian University of Health Sciences (submittedVersion)
- Funders
- European Regional Development Fund (J05-LVPA-K-01-0232)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- virtual reality exposure therapy, mental stress detection, physiological signals, skin conductance, skin temperature, public speaking anxiety, signal fusion, support vector machine
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