{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T21:11:30Z","timestamp":1777669890765,"version":"3.51.4"},"reference-count":70,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 111- 2634-F-002-021"],"award-info":[{"award-number":["NSTC 111- 2634-F-002-021"]}],"id":[{"id":"10.13039\/501100020950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Application<\/jats:title>\n                    <jats:p>The established models can be used in realistic driving scenarios.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00187208231183874","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T05:58:56Z","timestamp":1688104736000},"page":"1681-1702","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability"],"prefix":"10.1177","volume":"66","author":[{"given":"Cheng-Yu","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Imperial College London, London, UK"}]},{"given":"He-in","family":"Cheong","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Imperial College London, London, UK"}]},{"given":"Robert","family":"Houghton","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Imperial College London, London, UK"}]},{"given":"Wen-Hua","family":"Hsu","sequence":"additional","affiliation":[{"name":"School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan"}]},{"given":"Kang-Yun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan"}]},{"given":"Jiunn-Horng","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan"},{"name":"Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan"},{"name":"Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan"}]},{"given":"Yi-Chun","family":"Kuan","sequence":"additional","affiliation":[{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan"},{"name":"Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan"},{"name":"Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan"},{"name":"Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan"},{"name":"Dementia Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan"}]},{"given":"Hsin-Chien","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan"}]},{"given":"Cheng-Jung","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan"}]},{"given":"Lok-Yee Joyce","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medicine, Shin Kong Wu-Ho-Su Memorial Hospitall, Taipei, Taiwan"}]},{"given":"Yin-Tzu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan"}]},{"given":"Shang-Yang","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan"},{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan"}]},{"given":"Iulia","family":"Manole","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Imperial College 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