Neural Processing Of Biomedical Data For
Improving Driving Safety
Author(s)
F. Barcellona, F. Filippi, M. Panella, A. M. Bersani
& A. Alessandrini
Abstract
Driving ability can be impaired by fatigue, drowsiness, drugs and alcohol, all of
which have been implicated in causing road traffic accidents. Acute
hypoglycaemia, which is the most common side effect of insulin therapy in
individuals with diabetes, may also compromise driving skills. Other than by
forbidding people to drive, the potential danger can be reduced by monitoring
health and consciousness of drivers, by providing them with feedback on their
conditions using, eventually, an emergency centre or biofeedback. In this paper,
we propose the use of a signal processing system based on neural networks for
system modelling and prediction. In particular, using neural networks we will
reproduce the glucose temporal evolution without invasive technique for drivers,
with the aim of preventing loss of consciousness while driving and hence
improving road safety. Some illustrative trials will be shown in this regard. This
research work is supported by the \“CTL Excellence Centre (Centro di Ricerca
sul Trasporto e la Logistica)” co-funded by the Italian Ministry of University,
Education and Research and by the University of Rome \“La Sapienza”.
Keywords: modelling and prediction, diabetes, data acquisition and analysis,
neural network, road safety.
1 Introduction
Driving is an activity that demands complex psychomotor skills, good
visuospatial functions, rapid information processing, vigilance, and satisfactory
judgment. Hypoglycaemia is a common side-effect of insulin therapy for
Keywords
modelling and prediction, diabetes, data acquisition and analysis,
neural network, road safety.
Related Book
Other papers in this volume
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