{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T08:21:54Z","timestamp":1778401314086,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centro Protesi INAIL (Istituto Nazionale per l\u2019Assicurazione contro gli Infortuni sul Lavoro)","award":["PR19-SV-P1"],"award-info":[{"award-number":["PR19-SV-P1"]}]},{"name":"Italian Ministry of Health","award":["PR19-SV-P1"],"award-info":[{"award-number":["PR19-SV-P1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensors are widely used to gather psychophysiological data in the laboratory and real-world applications. However, the accuracy of these devices should be carefully assessed. The study focused on testing the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Fourteen healthy subjects were recruited for the experimental campaign, where HRV and EDA were recorded over six experimental conditions (Baseline, Video Clip, Scream, No-Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The overall quality of the E4 data was investigated; agreement and reliability were assessed by performing a Bland\u2013Altman analysis and by computing the Spearman\u2019s correlation coefficient. HRV time-domain parameters reported high reliability levels in Baseline (r &gt; 0.72), Video Clip (r &gt; 0.71), and No-Risk Driving (r &gt; 0.67), while HRV frequency domain parameters were sufficient in Baseline (r &gt; 0.58), Video Clip (r &gt; 0.59), No-Risk (r &gt; 0.51), and Low-Risk Driving (r &gt; 0.52). As for the EDA parameters, no correlation was found. Further studies could enhance the HRV and EDA quality through further optimizations of the acquisition protocol and improvement of the processing algorithms.<\/jats:p>","DOI":"10.3390\/s23208423","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T14:22:17Z","timestamp":1697120537000},"page":"8423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Wrist-Worn Sensor Validation for Heart Rate Variability and Electrodermal Activity Detection in a Stressful Driving Environment"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5104-6733","authenticated-orcid":false,"given":"Simone","family":"Costantini","sequence":"first","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3613-361X","authenticated-orcid":false,"given":"Mattia","family":"Chiappini","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2362-3839","authenticated-orcid":false,"given":"Giorgia","family":"Malerba","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9037-1766","authenticated-orcid":false,"given":"Carla","family":"Dei","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"given":"Anna","family":"Falivene","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8092-1499","authenticated-orcid":false,"given":"Sara","family":"Arlati","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, 23900 Lecco, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6648-1917","authenticated-orcid":false,"given":"Vera","family":"Colombo","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, 23900 Lecco, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2568-9735","authenticated-orcid":false,"given":"Emilia","family":"Biffi","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5977-8090","authenticated-orcid":false,"given":"Fabio Alexander","family":"Storm","sequence":"additional","affiliation":[{"name":"Scientific Institute I.R.C.C.S. \u201cE. Medea\u201d, 23842 Bosisio Parini, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bitkina, O.V., Kim, J., Park, J., Park, J., and Kim, H.K. (2019). Identifying traffic context using driving stress: A longitudinal preliminary case study. Sensors, 19.","DOI":"10.3390\/s19092152"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1518\/001872098779480569","article-title":"Driver Stress and Performance on a Driving Simulator","volume":"40","author":"Matthews","year":"1998","journal-title":"Hum. 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