Applying neural network analysis on heart rate variability data to assess driver fatigue

Patel, Mitesh; Lal, Sara; Kavanagh, Diarmuid; Rossiter, P.

doi:10.1016/j.eswa.2010.12.028

Cited by 361 publications

(207 citation statements)

References 22 publications

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“…The salient features of our stress level detection approach includes: (a) first work to the best of our knowledge focusing a developing country like India in terms of scenario design, subject population and road settings, (b) physiological data collected in realtime driving scenarios modeled the stress contributing factors into a multiclass problem instead of a binary class 9 , (c) an exhaustive set of physiological features (39 statistical, syntactic and spectral) were extracted representing driver's current physiological state, (d) instead of less number of subjects and a one-fold classification [6][7] , we used more subjects and performed analysis on single as well multi-turn drive data, and (e) six neural network 4-class classifiers were evaluated instead of generalizing a single classifier.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

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Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra-and inter-subject variability. Task-induced stress-trends were tracked using -based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.

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Section: Introductionmentioning

confidence: 99%

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Singh¹,

Conjeti²,

Banerjee³

2014

IJCIS

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“…Nonlinear dynamical analysis techniques derived from the theory of nonlinear dynamical systems such as the correlation integral, Lyapunov exponents, and correlation dimension have been recently used in a number of fields of application. Assessment of driver's fatigue is one of the special application areas [4], [11], [12].…”

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confidence: 99%

Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Zhang

Wang

2014

IEEE Trans. Intell. Transport. Syst.

186

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Abstract-This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.Index Terms-Driver fatigue, electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), entropy, neural network.

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“…An arbitrarily chosen 9-neuron hidden layer is used. Fatigue classification results are coincidental with [24]. Derivative to fatigue, the level of driver's alertness is evaluated in much the same way by authors of the work [26].…”

Section: Driver Behaviour and Autonomous Vehiclesmentioning

confidence: 99%

Neural Networks in Transportation Research – Recent Applications

Pamuła¹

2017

Transport Problems

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Summary. Neural networks' (NNs) capability of mapping the nonlinear functions of variables describing the behaviour of objects and the simplicity of designing their configuration favours their applications in transport. This paper presents representative examples in the scope of prediction of road traffic parameters, road traffic control, measurement of road traffic parameters, driver behaviour and autonomous vehicles, and transport policy and economics. The features of the solutions are examined. The review shows that feedforward multilayer neural networks are the most often utilised configurations in transportation research. No systematic approach is reported on the optimisation of the NN configurations to achieve a set level of performance in solving modelling tasks. ZASTOSOWANIE SIECI NEURONOWYCH W BADANIACH W TRANSPORCIE -OSTATNIE OPRACOWANIAStreszczenie. Zdolność sieci neuronowej do odwzorowania nieliniowych zależności między zmiennymi, które opisują zachowanie obiektów, oraz łatwość opracowania efektywnej konfiguracji sprzyjają zastosowaniom ich w transporcie. W artykule przedstawiono reprezentatywne przykłady z zakresu: predykcji parametrów ruchu drogowego, sterowania ruchem drogowym, pomiarów parametrów ruchu, zachowania kierowców i prowadzenia autonomicznych pojazdów, ekonomii i polityki transportowej, oraz omówiono własności proponowanych rozwiązań. Przegląd wskazuje, że najczęściej wybieranymi sieciami neuronowymi są sieci jednokierunkowe wielowarstwowe. Brak jest systematycznego podejścia do optymalizacji konfiguracji sieci w celu osiągnięcia zadanego poziomu dokładności w zadaniach modelowania.

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Applying neural network analysis on heart rate variability data to assess driver fatigue

Cited by 361 publications

References 22 publications

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Neural Networks in Transportation Research – Recent Applications

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