Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification |
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| Affiliation: | 1. National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, Greece;2. Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich 80333, Germany;1. Department of Health and Rehabilitation Sciences, University of Western Ontario, 1201 Western Road, London, Ontario N6G 1H1, Canada;2. Department of Occupational Therapy, University of Florida, College of Public Health and Health Professions, Gainesville, FL, United States;3. Driver Rehabilitation Institute, Santa Rosa, CA, United States;4. All Dominion Driver, Kitchener, Ontario, Canada;5. Department of Statistical and Actuarial Sciences, University of Western Ontario, 1201 Western Road, London, Ontario N6G 1H1, Canada;6. Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, 339 Windermere Road, London, Ontario N6A 5A5 Canada;7. Schulich School of Medicine and Dentistry, Clinical Neurological Sciences, Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, 339 Windermere Road, London, Ontario N6A 5A5 Canada;1. School of Science, Monash University Malaysia, Malaysia;2. School of Pharmacy, Monash University Malaysia, Malaysia;3. Gerontology Laboratory, GA21 Platform, Monash University Malaysia, Malaysia;4. School of Pharmacy, Taylor’s University, Malaysia;5. Monash University Accident Research Centre, Australia;6. Division of Geriatrics, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia;7. School of Health Science, Swinburne University of Technology, Australia;1. Department of Family and Community Health, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania;2. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;3. Center for Injury Research and Prevention, The Children''s Hospital of Philadelphia, Philadelphia, Pennsylvania;4. Highway Safety Research Center, University of North Carolina, Chapel Hill, North Carolina;5. University of Michigan Transportation Research Institute, Ann Arbor, Michigan;6. Department of Industrial Engineering & Engineering Management, Western New England University, Springfield, Massachusetts;7. Allan F. Williams, LLC, Bethesda, Maryland;1. Department of Civil Engineering, Babol Noshirvani University of Technology, Shariati Ave., PO Box: 4714871167, Babol, Iran;2. Faculty of Technology, Policy, and Management, Delft University of Technology, Delft 2628 BX, the Netherlands;1. The Ran Naor Foundation, Hod Hasharon 45240, Israel;2. Tel-Aviv University, Tel-Aviv 69978, Israel;3. Technion, Haifa 32000, Israel;4. Or Yarok, Hod Hasharon 45240, Israel |
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| Abstract: | Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression. |
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| Keywords: | Depression Driving simulator Random forests Time-series |
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