Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

Abstract

Studies have shown that driver drowsiness is one of the main causes of road accidents. It is estimated that 30% of road accidents are caused by driver drowsiness, which creates a need for driver drowsiness detection in modern vehicle systems. Previous works have shown the viability of using heart rate variability (HRV) for detecting the onset of driver drowsiness. HRV is obtained for electrocardiogram (ECG) signals, of which the power bands can be analysed to determine the physiological state of a person. This paper introduces a new method to detect driver drowsiness by classifying the power spectrum of a person's HRV data using Block-based Neural Networks (BbNN), which is evolved using Genetic Algorithm (GA). For most cases, regular Artificial Neural Networks (ANN) are not suitable for high speed and efficient hardware implementation. BbNNs are better candidates due to its regular block based structure, has relatively fast computational speeds, lower resource consumption, and equal classifying strength in comparison to other ANNs. Preliminary work has shown promising results with up to 99.99% classification accuracy using the proposed BbNN detection system for HRV data.Index Terms-Block-based neural networks (BbNNs); genetic algorithm (GA); heart rate variability (HRV); electrocardiogram (ECG); driver drowsiness;

Multi-source information fusion for drowsy driving detection based on wireless sensor networks

“…Studies [19] and [20] show that ECG will obviously decrease when a driver is fatigued, and the changes of HR (heart rate) do have a potential relationship to the degree of drowsiness of the driver.…”

Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

“…Also, the BbNN neurons are limited to only function as a feed-forward neural network. This paper will not get into in depth mathematical modelling details, and recommend reading [8], [16] instead.…”

“…A BbNN can easily be represented as a 2D array of blocks, each block is a basic processing element which contain multiple input/output neurons. Training is done with the help of an evolutionary algorithm (EA), such as GA. BbNNs have been successfully used in various applications such as ECG signal classification [13], hypoglycaemia detection [14], pattern recognition [15], heart rate variation [16] and many more [9].…”

Co-simulation methodology for improved design and verification of hardware neural networks

“…This also allows complex models that are applicable a variety of applications to be quickly designed, such as industrial motor controllers or fuzzy systems. In this work, the case studies employed during the verification process are the XOR problem, driver drowsiness classification (published in [5]), and heart arrhythmia classification (published in [6]). …”