Scientific Literature citing Empatica's Devices


The ways in which Empatica’s devices can help revolutionize health are profound. Our goal is to provide advanced technological solutions to as many people as possible.


Empatica’s devices, with it’s EDA sensors, have been used in previous studies to monitor the sympathetic nervous system. These studies used EDA in the fields of drug efficacy, addiction, stress, PTSD, feelings based on music, etc. You can find a selection of studies at the bottom of this article.


You can read more about the science behind Embrace here.





Gouverneur, P., Jaworek-Korjakowska, J., Köping, L., Shirahama, K., Kleczek, P., & Grzegorzek, M. (2017). Classification of Physiological Data for Emotion Recognition. In Artificial Intelligence and Soft Computing (pp. 619–627). Springer, Cham.


Winslow, B. D., Chadderdon, G. L., Dechmerowski, S. J., Jones, D. L., Kalkstein, S., Greene, J. L., & Gehrman, P. (2016). Development and Clinical Evaluation of an mHealth Application for Stress Management. Public Mental Health, 130.


Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2016). Continuous Stress Detection Using a Wrist Device: In Laboratory and Real Life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1185–1193). New York, NY, USA: ACM.


Mühlbacher-Karrer, S., Mosa, A. H., Faller, L. M., Ali, M., Hamid, R., Zangl, H., & Kyamakya, K. (2017). A Driver State Detection System-Combining a Capacitive Hand Detection Sensor With Physiological Sensors. IEEE Transactions on Instrumentation and Measurement, PP(99), 1–13.


Leonard, N. R., Silverman, M., Sherpa, D. P., Naegle, M. A., Kim, H., Coffman, D. L., & Ferdschneider, M. (2017). Mobile Health Technology Using a Wearable Sensorband for Female College Students With Problem Drinking: An Acceptability and Feasibility Study. JMIR MHealth and UHealth, 5(7), e90.


Furberg, R. D., Taniguchi, T., Aagaard, B., Ortiz, A. M., Hegarty-Craver, M., Gilchrist, K. H., & Ridenour, T. A. (2017). Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations. JMIR Research Protocols, 6(3), e44.


Winslow, B. D., Chadderdon, G. L., Dechmerowski, S. J., Jones, D. L., Kalkstein, S., Greene, J. L., & Gehrman, P. (2016). Development and Clinical Evaluation of an mHealth Application for Stress Management. Public Mental Health, 130.


Miranda, D., Favela, J., & Ibarra, C. (2015). Detecting State Anxiety When Caring for People with Dementia. In J. Bravo, R. Hervás, & V. Villarreal (Eds.), Ambient Intelligence for Health (pp. 98–109). Springer International Publishing.


Gaggioli, A., Pallavicini, F., Morganti, L., Serino, S., Scaratti, C., Briguglio, M., … Riva, G. (2014). Experiential virtual scenarios with real-time monitoring (interreality) for the management of psychological stress: a block randomized controlled trial. Journal of Medical Internet Research, 16(7), e167.


Sevil, M., Hajizadeh, I., Samadi, S., Feng, J., Lazaro, C., Frantz, N., … Cinar, A. (2017). Social and competition stress detection with wristband physiological signals. In 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 39–42).


Holmgård, C., Yannakakis, G. N., Martínez, H. P., Karstoft, K.-I., & Andersen, H. S. (2014). Multimodal PTSD characterization via the StartleMart game. Journal on Multimodal User Interfaces, 9(1), 3–15.


Felipe, S., Singh, A., Bradley, C., Williams, A. C., & Bianchi-Berthouze, N. (2015). Roles for personal informatics in chronic pain. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2015 9th International Conference on (pp. 161–168). Istanbul, Turkey: IEEE.


Iranzo, R. M. G., Gomà, J. V., & Pascual, F. V. (2016). Managing Emotions for the Treatment of Patients with Chronic Low Back Pain. In Proceedings of the XVII International Conference on Human Computer Interaction (pp. 30:1–30:2). New York, NY, USA: ACM.


Koskimäki, H., Mönttinen, H., Siirtola, P., Huttunen, H.-L., Halonen, R., & Röning, J. (2017). Early Detection of Migraine Attacks Based on Wearable Sensors: Experiences of Data Collection Using Empatica E4. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 506–511). New York, NY, USA: ACM.


Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Sánchez Fernández, I., Klehm, J., … Loddenkemper, T. (2014). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy & Behavior, 37, 291–307.


Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Paesschen, W. V., … Hunyadi, B. (2017). Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment. Sensors, 17(10), 2338.


Heldberg, B. E., Kautz, T., Leutheuser, H., Hopfengärtner, R., Kasper, B. S., & Eskofier, B. M. (2015). Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5593–5596).


Van de Vel, A., Cuppens, K., Bonroy, B., Milosevic, M., Jansen, K., Van Huffel, S., … Ceulemans, B. (2016). Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update. Seizure, 41, 141–153.


Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I. S., Pearl, P. L., & Loddenkemper, T. (2016). Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, 88–101.


Cogan, D., Birjandtalab, J., Nourani, M., Harvey, J., & Nagaraddi, V. (2016). Multi-Biosignal Analysis for Epileptic Seizure Monitoring. International Journal of Neural Systems, 27(01), 1650031.


DeGiorgio, C. M., Curtis, A., Hertling, D., & Moseley, B. D. (2019). Sudden unexpected death in epilepsy: Risk factors, biomarkers, and prevention. Acta Neurologica Scandinavica, 139(3), 220–230.


Stewart, C. L., Rashid, Z., Ranjan, Y., Sun, S., Dobson, R. J. B., & Folarin, A. A. (2018). RADAR-base: Major Depressive Disorder and Epilepsy Case Studies. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 1735–1743). New York, NY, USA: ACM.


Zhao, X., & Lhatoo, S. D. (2018). Seizure detection: do current devices work? And when can they be useful? Current Neurology and Neuroscience Reports, 18(7), 40.


Carreiro, Stephanie & Chai, Peter & Carey, Jen & Lai, Jeffrey & Smelson, David & Boyer, Edward. (2018). mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder. Current Addiction Reports. 5. 10.1007/s40429-018-0192-0.


Carreiro S, Smelson D, Ranney M, et al. Real-time mobile detection of drug use with wearable biosensors: a pilot study. J Med Toxicol. 2014;11(1):73-9.


Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer EW. Wearable Biosensors to Detect Physiologic Change During Opioid Use. J Med Toxicol. 2016;12(3):255-62.


Carreiro S, Fang H, Zhang J, et al. iMStrong: Deployment of a Biosensor System to Detect Cocaine Use. J Med Syst. 2015;39(12):186.


Chintha KK, Indic P, Chapman B, Boyer EW, Carreiro S. Wearable Biosensors to Evaluate Recurrent Opioid Toxicity After Naloxone Administration: A Hilbert Transform Approach. Proc Annu Hawaii Int Conf Syst Sci. 2018;2018:3247-3252.


Leonard, N. R., Silverman, M., Sherpa, D. P., Naegle, M. A., Kim, H., Coffman, D. L., & Ferdschneider, M. (2017). Mobile Health Technology Using a Wearable Sensorband for Female College Students With Problem Drinking: An Acceptability and Feasibility Study. JMIR MHealth and UHealth, 5(7), e90.


Md Shaad Mahmud, Hua Fang, Stephanie Carreiro, Honggang Wang, Edward W. Boyer. Wearables technology for drug abuse detection: A survey of recent advancement. Smart Health. 2018.


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).


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. International Conference on Persuasive Technology (pp. 3-15). Springer, Cham.


Plus this completed clinical trial on detecting opioid use:


Singh, R., Lewis, B., Chapman, B., Carreiro, S., & Venkatasubramanian, K. (2019). A Machine Learning-based Approach for Collaborative Non-Adherence Detection during Opioid Abuse Surveillance using a Wearable Biosensor. Biomedical Engineering Systems and Technologies, International Joint Conference, BIOSTEC ... Revised Selected Papers. BIOSTEC (Conference), 5, 310–318.



Sano, A., Picard, R. W., & Stickgold, R. (2014). Quantitative analysis of wrist electrodermal activity during sleep. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 94(3), 382–389.


Rani A. Sarkis, Nirajan Puri, Swapna Putta, Jonathan Pham, Chiara Caborni, Francesco Onorati, Giulia Regalia, Rosalind Picard, Milena Pavlova, Robert Stickgold. (2018) Sleep and Memory Consolidation in Older Patients with Epilepsy: a Neurophysiologic Analysis. AES-poster-2018-Abstract2.pdf


Siirtola, P., Koskimäki, H., Mönttinen, H., & Röning, J. (2018). Using Sleep Time Data from Wearable Sensors for Early Detection of Migraine Attacks. Sensors (Basel, Switzerland), 18(5).



Gashi, S., Di Lascio, E., & Santini, S. (2019). Using Unobtrusive Wearable Sensors to Measure the Physiological Synchrony Between Presenters and Audience Members. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3(1), 13:1–13:19. a13-Gashi.pdf



Goodall G, Ciobanu I, Taraldsen K, Sørgaard J, Marin A, Drăghici R, Zamfir MV, Berteanu M, Maetzler W, Serrano JA

The Use of Virtual and Immersive Technology in Creating Personalized Multisensory Spaces for People Living With Dementia (SENSE-GARDEN): Protocol for a Multisite Before-After Trial

JMIR Res Protoc 2019;8(9):e14096  DOI: 10.2196/14096



Cella, Matteo & Okruszek, Łukasz & Lawrence, Megan & Zarlenga, Valerio & He, Zhimin. (2017). Using wearable technology to detect the autonomic signature of illness severity in schizophrenia. Schizophrenia Research. 195. 10.1016/j.schres.2017.09.028.





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