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

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