RT Conference Proceedings
T1 Estimating the stress for drivers and passengers using deep learning
A1 Corcoba Magaña, Víctor
A1 Muñoz Organero, Mario
A1 Arias Fisteus, Jesús
A1 Sánchez Fernández, Luis
AB 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.
PB CEUR-WS.org
SN 1613-0073 (online)
YR 2016
FD 2016
LK https://hdl.handle.net/10016/25691
UL https://hdl.handle.net/10016/25691
LA eng
NO 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
NO The  research  leading  to  these  results  has  received funding    from    the    “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R funded by the Spanish MINECO,   from   the   grant   PRX15/00036   from   the Ministerio de Educación Cultura y Deporte.
DS e-Archivo
RD 17 jul. 2024