RT Journal Article
T1 Prediction of motorcyclist stress using a heartrate strap, the vehicle telemetry and road information
A1 Corcoba Magaña, Víctor
A1 Muñoz Organero, Mario
A1 Paneda, Xabiel G.
AB The number of motorcycles on the road has increased in almost all European countries according to Eurostat. Although the total number of motorcycles is lower than the number of cars, the accident rate is much higher. A large number of these accidents are due to human errors. Stress is one of the main reasons behind human errors while driving. In this paper, we present a novel mechanism to predict upcoming values for stress levels based on current and past values for both the driving behavior and environmental factors. First, we analyze the relationship between stress levels and different variables that model the driving behavior (accelerations, decelerations, positive kinetic energy, standard deviation of speed, and road shape). Stress levels are obtained utilizing a Polar H7 heart rate strap. Vehicle telemetry is captured using a smartphone. Second, we study the accuracy of several machine learning algorithms (Support Vector Machine, Multilayer Perceptron, Naive Bayes, J48, and Deep Belief Network) when used to estimate the stress based on our input data. Finally, an experiment was conducted in a real environment. We considered three different scenarios: home-workplace route, workplace-home route, and driving under heavy traffic. The results show that the proposal can estimate the upcoming stress with high accuracy. This algorithm could be used to develop driving assistants that recommend actions to prevent the stress.
PB IOS Press
SN 1876-1364
YR 2017
FD 2017-08-11
LK https://hdl.handle.net/10016/31785
UL https://hdl.handle.net/10016/31785
LA eng
NO The research leading to these results has receivedfunding from the “HERMES-SMART DRIVER”project TIN2013-46801-C4-2-R funded by theSpanish MINECO, from the grant PRX15/00036from the Ministerio de Educación Cultura y Deporteand from a sabbatical leave by the Carlos III ofMadrid University.
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RD 1 sept. 2024