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Empatica Inc empatica e4 device
Empatica E4 Device, supplied by Empatica Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/empatica+e4+device/e4+empatica/pm42096799-94-1-1
Average 86 stars, based on 1 article reviews
empatica e4 device - by Bioz Stars, 2026-09
86/100 stars

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Article Title: An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals.
Article Snippet: The constrained computational capabilities of the Empatica E4 device necessitate representing the model’s performance through the loss vs. epochs plot, enabling a more insightful and meaningful evaluation of the designed model, shown in Fig. 8.

Article Title: Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.
Article Snippet: 1Departments of Neurology and Physiology and Biomedical Engineering, Mayo Foundation, Rochester, Minnesota, USA 2School of Engineering, University of North Florida, Jacksonville, Florida, USA 3Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia 4Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia 5Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK 6Seer Medical, Melbourne, Victoria, Australia 7Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Melbourne, Victoria, Australia 8Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic 9Department of Neurosurgery, Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany 10Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany 11SLabs AI Pty Ltd., Melbourne, Australia

Article Title: Relationships between brain functional connectivity and resting cardiac autonomic profiles in functional neurological disorder: A pilot study.
Article Snippet: The Empatica E4 device was fitted on the participant’s left wrist, and data were streamed in real time to an iOS device running the E4 Connect app.

Article Title: An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals
Article Snippet: The constrained computational capabilities of the Empatica E4 device necessitate representing the model’s performance through the loss vs. epochs plot, enabling a more insightful and meaningful evaluation of the designed model, shown in Fig. .

Article Title: Stress in action wearables database: A database of noninvasive wearable monitors with systematic technical, reliability, validity, and usability information.
Article Snippet: For several wearables, the literature review produced a large number of hits (e.g., 530 papers for Empatica E4 device).

Article Title: An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals.
Article Snippet: Overall, the results suggest that the EEG Headband MUSE and Samsung Galaxy Watch provide more dependable performance compared to the Empatica E4 device.

Article Title: An ensemble deep learning framework for emotion recognition through wearable devices multi-modal physiological signals.
Article Snippet: In contrast, the Empatica E4 device reported an average classification accuracy of 49.75%, showing a high rate of misclassification for the nine discrete emotions.

Activity Assay:

Article Title: A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN
Article Snippet: .. Wrist heart rate, measured by the Empatica E4 device, serves as input data for the model. Additionally, demographic data, including biometric information such as height and weight, is input into the model to derive the activity intensity coefficient and calculate the RMR. ..



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