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Validating Measures of Electrodermal Activity and Heart Rate Variability Derived From the Empatica E4 Utilized in Research Settings That Involve Interactive Dyadic States
Open accessAbstract
Portable and wireless devices that collect physiological data are becoming more and more sought after in clinical and psychophysiological research as technology swiftly advances. These devices allow for data collection in interactive states, such as dyadic therapy, with reduced restraints compared to traditional laboratory devices. One such portable device is the Empatica E4 wristband (Empatica Srl, Milan, Italy) which allows quantifying cardiac interbeat intervals (IBIs), heart rate variability (HRV), and electro-dermal activity (EDA), as well as several other acceleration and temperature measures. In the current study, we aimed to assess IBI, HRV, and EDA measures, against the same data collected from the well-validated MindWare mobile impedance cardiograph device (MindWare Technology, Gahanna, OH, United States). We assessed the E4 strictly as a research instrument and not as a clinical tool. We were specifically interested in the wristbands' performance during naturalistic interactive face-to-face conversations which inherently involve more hand movements. We collected data from 30 participants, nested in 15 dyads, which were connected to both devices simultaneously, during rest and during a social conversation. After preprocessing and analyses, we found that mean IBIs obtained by the E4 and the MindWare device, were highly similar during rest and during conversation. Medium to high correlations were found between the devices with respect to several HRV measures, with higher correlations during rest compared to conversation. The E4 failed to produce reliable EDA data. We conclude by discussing the strengths and limitations of the E4 during seated conversational states and suggest optimal ways to collect and analyze data with the E4.
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“While a complete loss of contact results in a 0-line for the EDA signal, motion artifacts without contact loss result in abrupt level changes or high amplitude fluctuations, and PPG shows considerable deviation from expected signal patterns24,42.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [42]
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InfluentialCostantini, S., Chiappini, M., Malerba, G., Dei, C., Falivene, A., Arlati, S., Colombo, V. M., Biffi, E., & Storm, F. A. (2023) · Sensors · doi:10.3390/s23208423 · Cited by 31
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“The research by Milstein & Gordon (2020) was done to examine the validity of this particular device as a purely research tool compared to the MIndwave mobile impendence cardiographic device.”
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“In contrast, wearable sensors are mobile and wireless, and can be deployed in a naturalistic environment (Milstein & Gordon, 2020).”
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The importance of physiological data variability in wearable devices for digital health applications
Cosoli, G., Poli, A., Spinsante, S., & Scalise, L. (2022) · ACTA IMEKO · doi:10.21014/acta_imeko.v11i2.1135 · Cited by 9
“Previous studies underlined that the measurement is not reliable if compared to finger/palm skin conductivity [44]; in fact, thermoregulatory processes would affect the results more than psychophysiological phenomena, which on the contrary are more influencing in the standard measurement sites [45].”
BackgroundRavindran, K. K. G., della Monica, C., Atzori, G., Lambert, D., Revell, V. L., & Dijk, D. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871559 · Cited by 8
“uk studies employed few participants and were not performed in older adults during sleep [2]–[5].”
BackgroundReal-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications
Vila, G., Godin, C., Charbonnier, S., & Campagne, A. (2021) · Sensors · doi:10.3390/s21165357 · Cited by 8
“In the same vein, [26] compared IBI values and cardiac features from E4 with an ambulatory ECG device (VU–AMS) in clinical settings; and [27] collected cardiac features from E4 and another wearable ECG instrument (MindWare Mobile Device).”
BackgroundPhysiological Synchrony in a Collaborative Virtual Reality Task
Moharana, B., Keighrey, C., & Murray, N. (2023) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex58391.2023.10178661 · Cited by 11
“Empatica E4 Wristband: The E4 wristband was used to collect the physiological data from participants [31].”
MethodKönig, A., Müller, P., Tröger, J., Lindsay, H., Alexandersson, J., Hinze, J., Riemenschneider, M., Postin, D., Ettore, É., Lecomte, A., et al. (2022) · Personalized Medicine in Psychiatry · doi:10.1016/j.pmip.2022.100094 · Cited by 9
“The heart rate will be automatically calculated with a proprietary algorithm based on the blood volume pulse recorded from a wristband [87].”
Moebus, M., & Holz, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305258 · Cited by 9
Paloniemi, S., Penttonen, M., Eteläpelto, A., Hökkä, P. K., & Vähäsantanen, K. (2022) · Professional and practice-based learning · doi:10.1007/978-3-031-08518-5_5 · Cited by 11
The Mitigating Effects of Water Sound Attributes on Stress Responses to Traffic Noise
Deng, L., Rising, H. H., Gu, C., & Bimal, A. (2023) · Environment and Behavior · doi:10.1177/00139165241245829 · Cited by 9
“This wearable wireless device allows for real-time computerized biofeedback and data collection, enabling the extrapolation of lab-based research findings to real-world settings (Milstein & Gordon, 2020; Schuurmans et al., 2020).”
de Looff, P. C., Noordzij, M. L., Nijman, H. L. I., Goedhard, L. E., Bogaerts, S., & Didden, R. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1330993 · Cited by 7
“EDA is useful for strong and sustained stressors (63), but the reliability and validity compared to criterion devices is uncertain (61).”
BackgroundCamerini, A.-L., Marciano, L., Annoni, A. M., Ort, A., & Petrocchi, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.840845 · Cited by 9
“Validation studies showed that the E4 wristband produced reliable HR data (Schuurmans et al., 2020) but less reliable EDA data (Milstein and Gordon, 2020) compared to other physiological yet less userfriendly measurement tools.”
ResultCosoli, G., Mansi, S. A., Revel, G. M., & Arnesano, M. (2023) · ACTA IMEKO · doi:10.21014/actaimeko.v12i3.1570 · Cited by 7
Coping with emotional pain: An experimental comparison of acceptance vs. avoidance coping
Konstantinou, P., Trigeorgi, A., Georgiou, C., Michaelides, M. P., Gloster, A. T., McHugh, L., Panayiotou, G., & Karekla, M. (2024) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2024.100820 · Cited by 9
Virtual Reality in Dark Tourism: Multisensory Virtual Tourism Experiences with Thermal Stimuli
Wang, J., Santoso, H. B., Changkaew, L., Tamtama, G. I. W., & Windasari, N. A. (2025) · Journal on Computing and Cultural Heritage · doi:10.1145/3720398 · Cited by 8
“[ 74].”
InfluentialMansi, S. A., Cosoli, G., Pisello, A. L., Pigliautile, I., Revel, G. M., & Arnesano, M. (2022) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot54413.2022.9831610 · Cited by 7
On the Impact of COVID-19 on Subjective Digital Media Quality Assessment
Zepernick, H., Pieper, K., Spang, R. P., Engelke, U., Hirth, M., & Naderi, B. (2021) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp53017.2021.9733583 · Cited by 7
PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user StudiesPreprint
Joshi, J., Wang, K., & Cho, Y. (2023) · Preprints.org · doi:10.20944/preprints202308.0545.v1 · Cited by 7
Yan, L., Gašević, D., Echeverría, V., Zhao, L., Jin, Y., Li, X., & Martínez‐Maldonado, R. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00475-9 · Cited by 7
“In contrast, heart rate data, even when collected via wearable sensors, has demonstrated reliability comparable to medical-grade devices (Milstein & Gordon, 2020).”
Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications
Tsirmpas, C., Konstantopoulos, S., Andrikopoulos, D., Kyriakouli, K., & Fatouros, P. (2025) · Sensors · doi:10.3390/s25144406 · Cited by 7
“…devices such as the Feel Monitor [10], Empatica E4 [11], and Shimmer3 GSR+ [12] have been widely adopted in clinical and research settings [13, 14, 15, 16, 17, 18], while consumer-grade devices like the Fitbit and Garmin wearables are increasingly being used in mental health monitoring…”
Validity of the Polar H10 for Continuous Measures of Heart Rate and Heart Rate Synchrony Analysis
Chung, V., Chopin, L., Karadayi, J., & Grèzes, J. (2026) · Sensors · doi:10.3390/s26030855 · Cited by 7
“One exception is Milstein and Gordon [13], who validated the accuracy of the Empatica E4 wristband against mobile ECG in naturalistic dyadic interactions.”
Li, D., Lee, C., Park, A. H., Lee, H., & Ding, Y. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1019885 · Cited by 6
Saini, G., Zain, M., Noronha, M., Bonin, R. P., & Lomanowska, A. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-51773-6 · Cited by 9
“The Empatica E4 has been shown to produce reliable and consistent HR and HRV data when compared to other validated tools 68,69 .”
ResultCharlton, P. H., Prada, E. J. A., Mant, J., & Kyriacou, P. A. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adb89e · Cited by 8
BraidFlow: A Flow-annotated Dataset of Kumihimo Braidmaking Activity
Zaman, A., Endow, S., Rakib, M. A. N., & Torres, C. (2023) · Designing Interactive Systems Conference · doi:10.1145/3563657.3596026 · Cited by 6
“Although we planned to place the E4 wristband on the non-dominant hand to minimize motion artifacts[46], we encountered that participants often held”
MethodHasselman, F., den Uil, L., Koordeman, R., de Looff, P. C., & Otten, R. (2023) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1289983 · Cited by 6
Frawley, C., & Taylor, D. D. (2024) · Journal of Counseling & Development · doi:10.1002/jcad.12500 · Cited by 8
Effects of labyrinth-like path designs on mitigating stress response to traffic noise
Deng, L., Rising, H. H., Gu, C., & Bimal, A. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111914 · Cited by 6
Melton, K. K., Hodge, C. J., Densley, K., & Sturdivant, R. X. (2024) · Journal of Leisure Research · doi:10.1080/00222216.2024.2315051 · Cited by 5
“Scosche Rhythm sensors use the Valencell® biometric chip, which has been benchmarked for providing above 99% reliability (Milstein and Gordon, 2020; Stahl et al., 2016).”
Zhou, Y., Parreira, J. D., Shahrbabak, S. M., Sanchez-Perez, J. A., Rahman, F. N., Gazi, A. H., Inan, O. T., & Hahn, J. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3453121 · Cited by 5
Toner, E. R., Rucker, M., Wang, Z., Larrazabal, M. A., Cai, L., Datta, D., Lone, H. R., Boukhechba, M., Teachman, B. A., & Barnes, L. E. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3562787 · Cited by 12
“This suggests that EDA frequency domain features may be robust to movement artifacts, a commonly observed problem with Empatica E4 EDA sensors [65].”
InfluentialRho, G., Rienzo, F. D., Marinai, C., Giannetti, F., Arcarisi, L., Bufano, P., Zanoletti, M., Righetti, F., Vallati, C., Laurino, M., et al. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-49062-0_3 · Cited by 5
Stritzelberger, J., Kirmse, M., Borutta, M. C., Gollwitzer, S., Reindl, C., Welte, T. M., Hamer, H. M., & Koehn, J. (2025) · Diagnostics · doi:10.3390/diagnostics15202604 · Cited by 4
Kondaurova, M. V., Smith, A. F., Mishra, R., Zheng, Q., Kondaurova, I., Francis, A. L., & Sallee, E. (2024) · American Journal of Audiology · doi:10.1044/2024_aja-24-00078 · Cited by 4
Validation of WeBe Band During Physical Activities
Fang, R., Hang, S., Zhang, R., Fang, C., Rafatirad, S., Hostinar, C. E., & Homayoun, H. (2024) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn63547.2024.10780730 · Cited by 4
Siche-Pantel, F., Mühlenberg, M., Buschfort, R., Michels, H., Jakobsmeyer, R., Oesterschlink, J., & Reinsberger, C. (2025) · BMC Neurology · doi:10.1186/s12883-025-04281-7 · Cited by 4
“In sitting rest positions, it shows high validity in heart rate variability and medium validity in electrodermal activity measurements [24].”
Nijman, H. L. I., Jakobs, A., Waters, M., Osborne, N., Moerbeek, M., Herstel, A., & de Looff, P. C. (2022) · Psychology of Music · doi:10.1177/03057356221111823 · Cited by 4
“Validation studies of assessments made with the Empatica E4 have yielded positive results (e.g., Menghini et al., 2019; Milstein & Gordon, 2020; Schuurmans et al., 2020; van Lier et al., 2020).”
Result
Metadata
- Authors
- Milstein, N., & Gordon, I.
- Affiliations
- Bar-Ilan University (IL)
- Corresponding author
- Gordon, Ilanit
- Venue
- Frontiers in Behavioral Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2020-08-18
- Volume
- 14
- Pages
- 148-148
- ISSN
- 1662-5153 (ISSN-L)
- OpenAlex
- W3058974884
- Cited by
- 180
- References
- 43
- Citations by year
- 2026: 28 · 2025: 29 · 2024: 32 · 2023: 44 · 2022: 29 · 2021: 17 · 2020: 1
- FWCI
- 14.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Behavioral Neuroscience (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Israel Science Foundation
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- Empatica E4, electrodermal activity, heart rate variability, RR interval, device validation, dyadic therapy, face-to-face communication, naturalistic interaction, psychophysiological measurement, measurement reliability
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