Estimating the stress for drivers and passengers using deep learning

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ISSN: 1613-0073 (online)
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The number of vehicles in circulation has become a problem both for safety and for the citizens health. Public transport is a solution to reduce its impact on the environment. One of the keys to encourag e users to use it is to improve comfort. On the other hand, numerous studies highlight that drivers are more likely to suffer physical and psychological illnesses due to the sedentary nature of this work and workload . In this paper, we propose a model to p redict the stress level on drivers and passengers. The solution is based on deep learning algorithms. The proposal employs the Heart Rate Variability (HRV) and telemetry from the vehicle in order to anticipate the incoming stress . It has been validated in a real environment on distinct routes. The results show that it predict s the stress by 86 % on drivers and 92% on passengers. This algorithm could be used to develop driving assistants that recommend actions to smooth driving, reducing the work load and the passenger stress.
Proceedings of JARCA 2016: XVIII JARCA Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence: El Toyo, Almería (Spain), 23-29 June, 2016
Stress level prediction, Stress-friendly driving behavior, Stress level classification, Heart Rate Variability, Machine learning, Deep learning, Algorithms
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Falomir, Zoe; Ortega, Juan Antonio. Proceedings of the XVIII Workshop on Qualitative Systems and Applications in Diagnosis, Robotics and Ambient Intelligence, Almería, Spain, 23-29 June, 2016. Ceur Workshop Proceedings (pp. 1-6)