Empatica E4 wristband

The most comfortable and accurate wristband to monitor physiological signals in real-time.

Empatica E4 wristband

Unobtrusive Monitoring

Record in the lab or at home with no hassle.

Clinical quality observation

Empower your research with accurate data from everyday life.

Your data anywhere

Access and analyze your raw data with our secure cloud platform.

Develop your own apps

Use our mobile API to access real-time E4 data in your app.

E4 Sensors

PPG Sensor

Photoplethysmography Sensor - Measures Blood Volume Pulse (BVP), from which heart rate, heart rate variability (HRV), and other cardiovascular features may be derived

3-axis Accelerometer

Captures motion-based activity

Event Mark Button

Tags events and correlate them with physiological signals

EDA Sensor (GSR Sensor)

Electrodermal Activity Sensor - Used to measure sympathetic nervous system arousal and to derive features related to stress, engagement, and excitement.

Infrared Thermopile

Reads peripheral skin temperature

Internal Real-Time Clock

Temporal resolution up to 0.2 seconds in streaming mode
Specifications: Electrodermal Activity Sensor (EDA), Galvanic Skin Response Sensor (GSR), Photoplethysmography

The E4 wristband is a wearable wireless device designed for continuous, real-time data acquisition in daily life.

View E4 Pricing

E4 Demo Videos Series

Hardware Specifications

The E4 wristband is the result of years of research and development. We have worked with the best industrial designers to create the most accurate and comfortable wearable device.

Form Factor

Case: 44x40x16 mm
Wrist: 110 - 190 mm
Weight: 25 g


Streaming mode: 20+ h
Memory mode: 36+ h
Charging time: < 2 h

Bluetooth ® Smart

Low consumption and short range
communication technology

Flash Memory

60+ hours of data storage

Splash Resistant Materials

Band: polyurethane
Case: polycarbonate and glass fiber
Lenses: polycarbonate and silicon

Regulatory Compliance

CE Cert. No. 1876/MDD (93/ 42/EEC Directive, Medical Device class 2a)
FCC CFR 47 Part 15b
IC (Industry Canada)
E4 wristband Hardware Specifications

Our Clients

Our technology is used by the most influential companies and world-renowned research institutions.

E4 Working Modes

The E4 wristband can fit many situations: from laboratory settings to at-home analysis

1 Recording Mode

The E4 wristband stores data in its internal memory. The data are later downloaded via USB through the Empatica Manager for Windows and Mac.

2 Streaming Mode

The E4 wristband connects to a smartphone or desktop computer via Bluetooth. We offer Realtime App and mobile API for iOS and Android mobile devices and desktop integration for Windows and Mac.

3 Upload to Connect

Both modes upload the data recorded in Empatica's secure cloud platform - Empatica Connect - which allows users to easily access their data.

Empatica Realtime App

Connect the E4 wristband to your smartphone and tablet via Bluetooth® (both iOS and Android compatible) to view signals in real-time.

Visualize E4 wristband sensor data-streams in real-time

Pan and zoom to review signal data

Save data securely on your smartphone

Seamlessly upload data to the Empatica cloud platform

Empatica Realtime App for iOS and Android

Empatica Connect

View, organize, assess, and download all of your recorded data (from Recording Mode and Streaming Mode) on a secure cloud platform. View graphs of Electrodermal Activity (EDA) also known as Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), Acceleration, Heart Rate (HR), and Temperature.

Access your encrypted data from anywhere

View precise, timestamped sessions

Download raw data in CSV format

Compare session details and signal streams

Empatica Connect Dashboard

Empatica Manager

Connect the E4 wristband to your Windows PC or Mac, upload the data recorded to Empatica’s cloud storage and update the E4 wristband to the latest version of the software.

Download recorded data on your Windows PC or Mac

Seamlessly upload data to the Empatica cloud platform

Empatica Manager for Windows and Mac

Empatica API

You can build your custom app to suit your needs. On iOS, Android, PC and Mac.

Desktop Integration

Integrate the E4 wristband into your infrastructure.
Get real-time signals on your PC or Mac.
Filter and receive data in a custom format.

Mobile API

Develop apps that leverage real-time data from the Empatica devices.
Back-up streamed data to Empatica Connect.

Publications citing our research devices

Some of the research accomplished using Empatica's devices and citing Empatica.

Publications citing E-series wristbands

Please contact your sales representative to be inserted in this list.

Bidwell, J., Khuwatsamrit, T., Askew, B., Ehrenberg, J. A., & Helmers, S. (2015). Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies. Seizure, European Journal of Epilepsy. doi:10.1016/j.seizure.2015.09.006, Abstract, BibTeX.

Hernandez, J., McDuff, D., & Picard, R. W. (2015). BioInsights: Extracting Personal Data from “Still” Wearable Motion Sensors. Proceedings of Body Sensor Networks Conference, MIT, Cambridge, USA - June, 2015. PDF, BibTeX.

Song, M., & DiPaola, S. (2015). Exploring Different Ways of Navigating Emotionally Responsive Artwork in Immersive Virtual Environments. Presented at the Electronic Visualisation and the Arts (EVA 2015). Abstract, PDF, BibTeX.

Chen, W., Jaques, N., Taylor, S., Sano, A., Fedor, S., & Picard, R. W. (2015). Wavelet-Based Motion Artifact Removal for Electrodermal Activity. In Proceedings International Conference of the IEEE Engineering in Medicine and Biology Society. Milano, IT. PDF, BibTeX.

Muller, S., & Fritz, T. (2015). Stuck and Frustrated or In Flow and Happy: Sensing Developers’ Emotions and Progress. In 37th International Conference on Software Engineering. Florence, Italy, Abstract, PDF, BibTeX.

Hernandez, J., McDuff, D. J., & Picard, R. W. (2015). BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions. In Proceedings of Pervasive Health. Istanbul, Turkey, PDF, BibTeX.

Doty, T. J., Kellihan, B., Jung, T.-P., Zao, J. K., & Litvan, I. (2015). The Wearable Multimodal Monitoring System: A Platform to Study Falls and Near-Falls in the Real-World. In J. Zhou & G. Salvendy (Eds.), Human Aspects of IT for the Aged Population. Design for Everyday Life. Springer International Publishing. 412-422. Abstract, PDF, BibTeX.

Muller, S. C. (2015). Measuring Software Developers Perceived Difficulty with Biometric Sensors. In Proceedings of the 37th International Conference on Software Engineering - Piscataway, NJ, USA, 2, 887-890, Abstract, PDF, BibTeX.

Sano, A., & Picard, R. W. (2013). Stress Recognition Using Wearable Sensors and Mobile Phones. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 671-676. doi:10.1109/ACII.2013.117, Abstract, PDF, BibTeX.

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. doi:10.2196/jmir.3235, Abstract, PDF, BibTeX.

Ouwerkerk, M Martin, & Westerink, JHDM Joyce. (2014). Introduction to the symposium on ambulatory skin conductance. Abstract, PDF, BibTeX.

Gjoreski, M. (2015). Stress Detection Using Smartphone and Wearable Sensors. Master Degree in Information and Communication Technology. Jožef Stefan International Postgraduate School, Slovenia., PDF, BibTeX.

Muaremi, A., Arnrich, B., & Tröster, G. (2013). Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep. BioNanoScience, 3(2), 172–183. doi:10.1007/s12668-013-0089-2, Abstract, BibTeX.

Coenen, T., Coorevits, L., & Lievens, B. (2015). The wearable Living Lab: how wearables could support Living Lab projects. Presented at the Open Living Lab Days 2015, Istanbul, Turkey. Abstract, PDF, BibTeX.

Ollander, S. (2015). Wearable Sensor Data Fusion for Human Stress Estimation. Master thesis at Linkoping University, Sweden, Department of Electrical Engineering. PDF, BibTeX.

Rank, S., & Lu, C. (2015). PhysSigTK: Enabling Engagement Experiments with Physiological Signals for Game Design. Presented at the 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). PDF, BibTeX.

Foster, M. E. (2015). Natural face-to-face conversation with socially intelligent robots. In Proceedings of the {IROS} 2015 Workshop on Spatial Reasoning and Interaction for Real-World Robotics. Hamburg, Germany. PDF, BibTeX.

Noordzij, M. L., Dorrestijn, S. M., & van den Berg, I. A. (n.d.). An idiographic study into the physiology and self-reported mental workload of learning to drive a car. Abstract, BibTeX.

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. Presented at the Transportation Research Board 95th Annual Meeting. Abstract, BibTeX.

Villanueva, I., Valladares, M., & Goodridge, W. (2016). Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity. Journal of Visualized Experiments, (108). Abstract, BibTeX.

Jain, S., Oswal, U., Xu, K. S., Eriksson, B., & Haupt, J. (2016). A Compressed Sensing Based Decomposition of Electro-Dermal Activity Signals. Abstract, BibTeX.

Kucher, K., Cernea, D., & Kerren, A. (2016). Visualizing Excitement of Individuals and Groups Proceedings of EmoVis 2016, ACM IUI 2016 Workshop on Emotion and Visualization, Sonoma, CA, USA, March 10, 2016 (pp. 15-22). Abstract, BibTeX.

Parker, R. (2016). Your environment and you: investigating stress triggers and characteristics of the built environment Master of landscape architecture. Kansas State University, Manhattan, Kansas. Abstract, BibTeX.

Gervais, R., Roo, J. S., Frey, J., & Hachet, M. (2016). Introspectibles: Tangible Interaction to Foster Introspection. Presented at the Computing and Mental Health - CHI '16 Workshop. Abstract, BibTeX.

Tsiamyrtzis, P., Dcosta, M., Shastri, D., Prasad, E., & Pavlidis, I. (2016). Delineating the Operational Envelope of Mobile and Conventional EDA Sensing on Key Body Locations. Proceedings of the 2016 SIGCHI Conference on Human Factors in Computing Systems (CHI) Abstract, BibTeX.

Abbass, H. A., Leu, G., & Merrick, K. (2016). A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data. IEEE Access Abstract, BibTeX.

Xia, V., Jaques, N., Taylor, S., Fedor, S., & Picard, R. (2015). Active Learning for Electrodermal Activity Classification. Abstract, BibTeX.

Hemmelmann, J. (2016, January 19). Self-reported stress evaluation and physiological response. Bachelor Thesis. University of Twente. Abstract, BibTeX.

Harvey, M., Langheinrich, M., & Ward, G. Remembering through lifelogging: A survey of human memory augmentation. Pervasive and Mobile Computing. Abstract, BibTeX.

Peters, S., & Benasich, A. (2016, January). Novel Approaches to Measuring Infant Daytime Sleep Neurophysiology and Autonomic Activity. Presented at the All-Hands Meeting, Temporal Dynamics of Learning Center. BibTeX.

Publications citing Empatica

Callaway, J., & Rozar, T. (2015). Quantified Wellness, Wearable Technology Usage and Market Summary (Research Bulletin). RGA Reinsurance Company. Retrieved from Abstract, PDF, BibTeX.

Picard, R. W. (2015). Recognizing Stress, Engagement, and Positive Emotion. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI 2015), New York, NY, USA, 3-4. doi:10.1145/2678025.2700999, Abstract, BibTeX.

Felipe, S., Singh, A., Bradley, C., Williams, A., & Bianchi-Berthouze, N. (2015). Roles for Personal Informatics in Chronic Pain. 9th International Conference on Pervasive Computing Technologies for Healthcare, May 2015, Istanbul, Turkey. doi:10.4108/icst.pervasivehealth.2015.259501, Abstract, PDF, BibTeX.

Dolgin, E. (2014). Technology: dressed to detect. Nature, 511(7508), S16-S17. doi:10.1038/511S16a, Abstract, BibTeX.

Van Lieshout, M., Wiezer, N. M., & De Korte, E. (2015). The digital stress coach. Total control over your mental health, or "Big Brother is watching you?" In Sincere Support - The rise of the e-coach (1st ed), Vol. Chapter 6, 161-176. Abstract, BibTeX.

Caon, M., Tagliabue, M., Angelini, L., Perego, P., Mugellini, E., & Andreoni, G. (2014). Wearable Technologies for Automotive User Interfaces: Danger or Opportunity? In Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, New York, NY, USA, 1-5. doi:10.1145/2667239.2667314, Abstract, PDF, BibTeX.

Pijeira-Diaz, H. J., Drachsler, H., Jarvela, S., & Kirschner, P. A. (2016). Investigating Collaborative Learning Success with Physiological Coupling Indices Based on Electrodermal Activity. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 64-73). New York, NY, USA: ACM. Abstract, BibTeX.

Imre, D. (2016). Real-time Analysis of Skin Conductance for Affective Dynamic Difficulty Adjustment in Video Games. Algoma University. Abstract, BibTeX.

Jory, C., Shankar, R., Coker, D., McLean, B., Hanna, J., & Newman, C. (2016). Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure, 36, 4-15. Abstract, BibTeX.

Kuijpers, E., & Verdonk, A. (2015). Co-Creating new tools for good practices in recovery and high secure healing environments. In Proceedings Violence in Clinical Psychiatry. BibTeX.

Miranda, D., Favela, J., & Ibarra, C. (2015). Detecting State Anxiety When Caring for People with Dementia. In J. Bravo et al. (Eds.), Ambient Intelligence for Health (pp. 98-109). Springer International Publishing. Abstract, BibTeX.

Gilmore, R. O. (2016). From big data to deep insight in developmental science. Wiley Interdisciplinary Reviews: Cognitive Science, 2016. Abstract, BibTeX.

How to cite Empatica's research products

To cite Empatica's devices you can refer to the following publications.

E-series wristbands

Garbarino, M., Lai, M., Bender, D., Picard, R. W., & Tognetti, S. (2014). Empatica E3 - A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In 2014 EAI 4th International Conference on Wireless Mobile Communication and Healthcare (Mobihealth) 39-42. Abstract, PDF, BibTeX.

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