{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:00:05Z","timestamp":1782316805728,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Trade, Industry, and Energy, Republic of Korea","award":["20204010600090"],"award-info":[{"award-number":["20204010600090"]}]},{"name":"Ministry of Trade, Industry, and Energy, Republic of Korea","award":["R.G.P.2\/112\/43"],"award-info":[{"award-number":["R.G.P.2\/112\/43"]}]},{"name":"the Deanship of Scientific Research at King Khalid University","award":["20204010600090"],"award-info":[{"award-number":["20204010600090"]}]},{"name":"the Deanship of Scientific Research at King Khalid University","award":["R.G.P.2\/112\/43"],"award-info":[{"award-number":["R.G.P.2\/112\/43"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient\u2019s data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.<\/jats:p>","DOI":"10.3390\/s23083984","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T04:54:24Z","timestamp":1681448064000},"page":"3984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-1669","authenticated-orcid":false,"given":"Ahmad","family":"Almadhor","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-4409","authenticated-orcid":false,"given":"Gabriel Avelino","family":"Sampedro","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Studies, University of the Philippines Open University, Los Ba\u00f1os 4031, Philippines"},{"name":"Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4215-7260","authenticated-orcid":false,"given":"Mideth","family":"Abisado","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technologies, National University, Manila 1008, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8204-4896","authenticated-orcid":false,"given":"Sidra","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye-Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, HITEC University, Taxila 47080, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5564-6114","authenticated-orcid":false,"given":"Jamel","family":"Baili","sequence":"additional","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia"},{"name":"Higher Institute of Applied Science and Technology of Sousse (ISSATS), Cit\u00e9 Taffala (Ibn Khaldoun) 4003 Sousse, University of Sousse, Sousse 4000, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Hyuk","family":"Cha","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4653923","DOI":"10.1155\/2022\/4653923","article-title":"Machine learning for healthcare wearable devices: The big picture","volume":"2022","author":"Sabry","year":"2022","journal-title":"J. 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