{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T14:40:09Z","timestamp":1778769609915,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,3,2]],"date-time":"2015-03-02T00:00:00Z","timestamp":1425254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of  two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports.<\/jats:p>","DOI":"10.3390\/s150305136","type":"journal-article","created":{"date-parts":[[2015,3,2]],"date-time":"2015-03-02T10:45:12Z","timestamp":1425293112000},"page":"5136-5150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Biosignal Analysis to Assess Mental Stress in Automatic  Driving of Trucks: Palmar Perspiration and  Masseter Electromyography"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4346-2881","authenticated-orcid":false,"given":"Rencheng","family":"Zheng","sequence":"first","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeyuki","family":"Yamabe","sequence":"additional","affiliation":[{"name":"New Industry Creation Hatchery Center, Tohoku University, Sendai 980-8579, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kimihiko","family":"Nakano","sequence":"additional","affiliation":[{"name":"Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiro","family":"Suda","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,3,2]]},"reference":[{"key":"ref_1","unstructured":"Markoff, J. 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