Recent Publications citing the E4 wristband:

Updated:

Onton, J. A., Kang, D. Y., & Coleman, T. P. (2016). Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity. Frontiers in Human Neuroscience, 10. http://journal.frontiersin.org/article/10.3389/fnhum.2016.00605/full

 

Vicary, S., Sperling, M., von Zimmermann, J., Richardson, D. C., & Orgs, G. (2017). Joint action aesthetics. PLOS ONE, 12(7), e0180101. https://doi.org/10.1371/journal.pone.0180101

 

Corino, V. D., Laureanti, R., Ferranti, L., Scarpini, G., Lombardi, F., & Mainardi, L. (2017). Detection of atrial fibrillation episodes using a wristband device. Physiological Measurement. https://doi.org/10.1088/1361-6579/aa5dd7

 

McCarthy, C., Pradhan, N., Redpath, C., & Adler, A. (2016, May). Validation of the Empatica E4 wristband. In Student Conference (ISC), 2016 IEEE EMBS International (pp. 1-4). IEEE. https://doi.org/10.1109/EMBSISC.2016.7508621

 

Currie, J., Bond, R. R., McCullagh, P., Black, P., Finlay, D. D., & Peace, A. (2016, September). An eye-tracking assessment of coronary care nurses during the interpretation of patient monitoring scenarios. In Computing in Cardiology Conference (CinC), 2016 (pp. 105-108). http://www.cinc.org/archives/2016/pdf/034-515.pdf

 

Lutscher, D. (2016). The relationship between skin conductance and self-reported stress: does the relationship exist and, if so, does it differ across different types of stressors? (Bachelor's thesis, University of Twente). Retrieved from http://essay.utwente.nl/69969/

 

Matsubara, M., Augereau, O., Sanches, C. L., & Kise, K. (2016, December). Emotional arousal estimation while reading comics based on physiological signal analysis. In Proceedings of the 1st International Workshop on coMics ANalysis, Processing and Understanding (p. 7). ACM. https://doi.org/10.1145/3011549.3011556

 

Ollander, S. (2015). Wearable Sensor Data Fusion for Human Stress Estimation. Linkoping University, Linkoping, Sweden. Retrieved from http://www.diva-portal.org/smash/get/diva2:865706/FULLTEXT01.pdf

 

Kikhia, B., Stavropoulos, T. G., Andreadis, S., Karvonen, N., Kompatsiaris, I., Sävenstedt, S., ... & Melander, C. (2016). Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia. Sensors, 16(12), 1989. http://www.mdpi.com/1424-8220/16/12/1989/htm

 

Cabibihan, J. J., Javed, H., Aldosari, M., Frazier, T. W., & Elbashir, H. (2016). Sensing Technologies for Autism Spectrum Disorder Screening and Intervention. Sensors, 17(1), 46. http://www.mdpi.com/1424-8220/17/1/46/htm

 

Song, R., Liu, J., & Kong, X. J. (2016). Autonomic Dysfunction and Autism: Subtypes and Clinical Perspectives. North American Journal of Medicine and Science, 9(4). http://www.najms.com/index.php/najms/article/view/321/249

 

Cogan, D., Birjandtalab, J., Nourani, M., Harvey, J., & Nagaraddi, V. (2017). Multi-biosignal analysis for epileptic seizure monitoring. International Journal of Neural Systems, 27(01), 1650031. https://www.researchgate.net/profile/Javad_Birjandtalab/publication/300002992_Multi-Biosignal_Analysis_for_Epileptic_Seizure_Monitoring/links/5791031f08ae0831552f9457.pdf

 

Bulaj, G., M Ahern, M., Kuhn, A., S Judkins, Z., C Bowen, R., & Chen, Y. (2016). Incorporating natural products, pharmaceutical drugs, self-care and digital/mobile health technologies into molecular-behavioral combination therapies for chronic diseases. Current clinical pharmacology, 11(2), 128-145. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011401/

 

Shoval, N., Schvimer, Y., & Tamir, M. (2017). Real-Time Measurement of Tourists’ Objective and Subjective Emotions in Time and Space. Journal of Travel Research, 0047287517691155. http://journals.sagepub.com/doi/abs/10.1177/0047287517691155

 

Schubert, R., Welch, G., Daher, S., & Raij, A. (2016). HuSIS: A Dedicated Space for Studying Human Interactions. IEEE Computer Graphics and Applications, 36(6), 26-36. https://www.cs.unc.edu/~welch/media/pdf/Schubert2016aa.pdf

 

Danieli, M., Berra, E., Di Monaco, S., Fulcheri, C., Gosh, A., Perlo, E., ... & Veglio, F. (2016). [PP. 08.16] Automatically Classifying Essential Arterial Hypertension From Physiological and Daily Life Stress Responses. Journal of Hypertension, 34, e164. http://journals.lww.com/jhypertension/Abstract/2016/09002/_PP_08_16__AUTOMATICALLY_CLASSIFYING_ESSENTIAL.463.aspx

 

Lucci, D. (2016). Technology Enhances Social-Emotional Intelligence in Individuals with Autism Spectrum Disorders. Emotions, Technology, and Health, 151. https://books.google.com/books?hl=en&lr=lang_en&id=IyDfCQAAQBAJ&oi=fnd&pg=PA151&dq=empatica+E4+caregiving+&ots=meyUx1QZdY&sig=85StoXbYeUpE-epF1Xeguy81MxU

 

Ramesh, S. (2016). Using wearable technology to gain insight into children's physical and social behaviors (Doctoral dissertation, Massachusetts Institute of Technology). https://dspace.mit.edu/bitstream/handle/1721.1/106258/962181380-MIT.pdf?sequence=1

 

Cooper, T. N. (2016). Enhanced mutual performance monitoring to improve backup behaviors and team performance (Doctoral dissertation, Clemson University). http://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=3544&context=all_theses

 

Reyes, J. F. (2016). Effect of Emotion on Marketing Landing Page Conversion (Doctoral dissertation, University of Baltimore). https://mdsoar.org/bitstream/handle/11603/3819/UB_2016_Reyes_J.pdf?sequence=1&isAllowed=y

 

Gilmore, R. O. (2016). From big data to deep insight in developmental science. Wiley Interdisciplinary Reviews: Cognitive Science, 7(2), 112-126. http://onlinelibrary.wiley.com/doi/10.1002/wcs.1379/pdf

 

Engel, F., Bond, R., Keary, A., Mulvenna, M., Walsh, P., Zheng, H., ... & Hemmje, M. (2016, June). Sensecare: towards an experimental platform for home-based, visualisation of emotional states of people with dementia. In International Working Conference on Advanced Visual Interfaces (pp. 63-74). Springer International Publishing. http://link.springer.com/chapter/10.1007/978-3-319-50070-6_5

 

Alam, M. A. U., Roy, N., Holmes, S., Gangopadhyay, A., & Galik, E. (2016, June). Automated functional and behavioral health assessment of older adults with dementia. In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016 IEEE First International Conference on (pp. 140-149). IEEE. https://www.researchgate.net/profile/Mohammad_Arif_Ul_Alam/publication/303285094_Automated_Functional_and_Behavioral_Health_Assessment_of_Older_Adults_with_Dementia/links/573afdd408ae9f741b2d4e1d.pdf

 

Guribye, F., Gjøsæter, T., & Bjartli, C. (2016, October). Designing for Tangible Affective Interaction. In Proceedings of the 9th Nordic Conference on Human-Computer Interaction (p. 30). ACM. http://dl.acm.org/citation.cfm?id=2971547

 

Betancourt, M. A., Dethorne, L. S., Karahalios, K., & Kim, J. G. (2017). Skin Conductance as an In Situ Marker for Emotional Arousal in Children with Neurodevelopmental Communication Impairments: Methodological Considerations and Clinical Implications. ACM Transactions on Accessible Computing (TACCESS), 9(3), 8. http://social.cs.uiuc.edu/papers/betancourt_TACCESS.pdf

 

Bizzego, A., & Furlanello, C. (2017). DBD-RCO: Derivative Based Detection And Reverse Combinatorial Optimization To Improve Heart Beat Detection For Wearable Devices. bioRxiv, 118943. http://biorxiv.org/content/biorxiv/early/2017/03/21/118943.full.pdf

 

Ollander, S., Godin, C., Campagne, A., & Charbonnier, S. (2016, October). A comparison of wearable and stationary sensors for stress detection. In Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on (pp. 004362-004366). IEEE. http://ieeexplore.ieee.org/abstract/document/7844917/

 

Bui, T., Grayson, M., Hofer, K., McGuire, K., Morrow, M., Rodammer, N., ... & Patek, S. (2016, April). Remote patient monitoring for improving outpatient care of patients at risk for sepsis. In Systems and Information Engineering Design Symposium (SIEDS), 2016 IEEE (pp. 136-141). IEEE. http://ieeexplore.ieee.org/abstract/document/7489286/

 

Hoecherl, J., Schlegl, T., Berlehner, T., Kuhn, H., & Wrede, B. (2016, June). SmartWorkbench: Toward Adaptive and Transparent User Assistance in Industrial Human-Robot Applications. In ISR 2016: 47st International Symposium on Robotics; Proceedings of (pp. 1-8). VDE. http://ieeexplore.ieee.org/abstract/document/7559127/

 

Bexheti, A., Niforatos, E., Bahrainian, S. A., Langheinrich, M., & Crestani, F. (2016, September). Measuring the effect of cued recall on work meetings. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1020-1026). ACM. http://dl.acm.org/citation.cfm?id=2968563

 

Marchiori, E., Niforatos, E., & Preto, L. (2017). Measuring the Media Effects of a Tourism-Related Virtual Reality Experience Using Biophysical Data. In Information and Communication Technologies in Tourism 2017 (pp. 203-215). Springer, Cham. http://link.springer.com/chapter/10.1007/978-3-319-51168-9_15

 

Lee, Y., Masai, K., Kunze, K., Sugimoto, M., & Billinghurst, M. (2016, September). A Remote Collaboration System with Empathy Glasses. In Mixed and Augmented Reality (ISMAR-Adjunct), 2016 IEEE International Symposium on (pp. 342-343). IEEE. http://ieeexplore.ieee.org/abstract/document/7836533/

 

Spann, C. A., Schaeffer, J., & Siemens, G. (2017, March). Expanding the scope of learning analytics data: preliminary findings on attention and self-regulation using wearable technology. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 203-207). ACM. http://dl.acm.org/citation.cfm?id=3027427

 

Latoschik, M. E., Lugrin, J. L., Habel, M., Roth, D., Seufert, C., & Grafe, S. (2016, November). Breaking bad behavior: immersive training of class room management. In Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology (pp. 317-318). ACM. https://www.researchgate.net/profile/Jean-Luc_Lugrin/publication/309710710_Breaking_bad_behavior_immersive_training_of_class_room_management/links/5820be6308aeccc08af65daa.pdf

 

van Lier, H. G., Oberhagemann, M., Stroes, J. D., Enewoldsen, N. M., Pieterse, M. E., Schraagen, J. M. C., ... & Noordzij, M. L. (2017, April). Design Decisions for a Real Time, Alcohol Craving Study Using Physio-and Psychological Measures. In International Conference on Persuasive Technology (pp. 3-15). Springer, Cham. http://link.springer.com/chapter/10.1007/978-3-319-55134-0_1

 

Noordzij, Matthijs L. and Dorrestijn, Serena M. and Berg, Irma A. van den (2017) An idiographic study into the physiology and selfreported mental workload of learning to drive a car. In Human Factors and Ergonomics Society Europe Chapter 2016 Annual Conference, 26-10-2016 - 28-10-2016, Prague. http://doc.utwente.nl/102423/1/Noordzij2016_HFES.pdf

 

Kucher, K., Cernea, D., & Kerren, A. (2016, March). Visualizing excitement of individuals and groups. In Proceedings of the 2016 EmoVis Conference on Emotion and Visualization (pp. 15-22). Linkoping University. http://www.ep.liu.se/ecp/103/003/ecp16103003.pdf

 

Tsiamyrtzis, P., Dcosta, M., Shastri, D., Prasad, E., & Pavlidis, I. (2016, May). Delineating the Operational Envelope of Mobile and Conventional EDA Sensing on Key Body Locations. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5665-5674). ACM. https://pdfs.semanticscholar.org/e4c4/8e60546001a13cfe4c64d562a55c0ef50a0d.pdf

 

Spoletini, P., Brock, C., Shahwar, R., & Ferrari, A. (2016, September). Empowering Requirements Elicitation Interviews with Vocal and Biofeedback Analysis. In Requirements Engineering Conference (RE), 2016 IEEE 24th International (pp. 371-376). IEEE. http://ieeexplore.ieee.org/abstract/document/7765546/

 

Jain, S., Oswal, U., Xu, K., Eriksson, B., & Haupt, J. (2016). A Compressed Sensing Based Decomposition of Electrodermal Activity Signals. IEEE Transactions on Biomedical Engineering. https://arxiv.org/pdf/1602.07754

 

Saitis, C., & Kalimeri, K. (2016, July). Identifying urban mobility challenges for the visually impaired with mobile monitoring of multimodal biosignals. In International Conference on Universal Access in Human-Computer Interaction (pp. 616-627). Springer International Publishing. http://link.springer.com/chapter/10.1007/978-3-319-40238-3_59

 

Alstad, Z., Dahlstrom-Hakki, I., Asbell-Clarke, J., Rowe, E., & Altman, M. (2016). The Use of Multidimensional Biopsychological Markers to Detect Learning in Educational Gaming Environments. http://openscholar.mit.edu/sites/default/files/dept/files/request.pdf

 

Kim, E. S., On, K. W., Zhang, B. T., & Center, C. R. A. I. (2016). Deepschema: Automatic schema acquisition from wearable sensor data in restaurant situations. In Twenty-Fifth International Joint Conference on Artificial Intelligence (pp. 834-840). https://www.ijcai.org/Proceedings/16/Papers/123.pdf

 

Lo, J., Sehic, E., & Meijer, S. A. (2016). Mental Workload Measurements through Low-Cost and Wearable Sensors: Insights into Accuracy, Obtrusiveness, and Research Usability of Three Instruments. In Transportation Research Board 95th Annual Meeting (No. 16-2751).

 

Lin, L., & Jörg, S. (2016, March). The effect of realism on the virtual hand illusion. In Virtual Reality (VR), 2016 IEEE (pp. 217-218). IEEE. http://ieeexplore.ieee.org/abstract/document/7504731/

 

Spreeuwers, N. E. (2016). Arousing memory: Memories obtained from virtual reality are as correctly recalled as memories obtained from conventional two-dimensional screens (Master's thesis, University of Twente). http://essay.utwente.nl/71405/1/Spreeuwers.MA.Psychology.pdf

 

Shayea, A. (2017). The impact of bystanders on offenders: the presence of bystanders increases the likelihood of shoplifting (Master's thesis, University of Twente). http://essay.utwente.nl/71970/2/A.Shayea_%20MA_Psychology.pdf

 

Stroes, J. D. (2016). A feasibility study into measuring intraindividual alcohol craving in a longitudinal study: measuring self reported craving in a naturalistic setting combined with the usability of the Empatica E4 wristlet. (Bachelor's thesis, University of Twente). http://essay.utwente.nl/70233/

 

Samuel Mehr, Jennifer Kotler, David Haig, Max Krasnow (2017) Genomic imprinting is implicated in the psychology of music (In press, Psychological Science) https://osf.io/preprints/psyarxiv/6269f

 

Ognjen Rudovic, Jaeryoung Lee, Miles Dai, Björn Schuller, and Rosalind W. Picard  (2018) Personalized machine learning for robot perception of affect and engagement in autism therapy (Science Robotics  27 Jun 2018: Vol. 3, Issue 19, eaao6760 DOI: 10.1126/scirobotics.aao6760) 
http://robotics.sciencemag.org/content/3/19/eaao6760

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