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

Wei, Liang; Mukhopadhyay, Subhas Chandra; Jidin, Razali; Chen, Chia-Pang

doi:10.1109/icsenst.2013.6727771

Cited by 21 publications

(13 citation statements)

References 25 publications

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“…Krishnamoorthy et al [ 14 ] developed a drowsiness monitoring system to detect the HR and used the variation in HR to predict drowsiness using a photoplethysmograph (PPG) sensor. Wei et al [ 15 ] proposed an information-fusion-based drowsiness detection method based on the driver's eye activity, head inclination, sagging posture, heart beat rate, skin electric potential, EEG activities, gripping force on the steering wheel, and lane-keeping characteristics. Among the numerous physiological indicators available to estimate the driver's vigilance level, the EEG signal has been proved to be one of the most predictive and reliable [ 16 ] indicator compared to others.…”

Section: Introductionmentioning

confidence: 99%

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Lee

Chung

2014

Sensors

148

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Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

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Section: Introductionmentioning

confidence: 99%

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Lee

Chung

2014

Sensors

148

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“…For instance works such as [22] and [13], use multiple sensors to provide intelligent information on the driver's physiological signals, which can include eye activity measures, the inclination of the driver's face, heart rate monitoring, skin electric potential, and electroencephalographic (EEG) activities. In [2] is proposed a novel and non-intrusive driver behaviour detection system using a context-aware system combined with in-vehicle sensors collecting information regarding to vehicle's speed, acceleration, the direction of driver's eyes, the position in lane and the level of alcohol in the driver's blood.…”

Section: Related Workmentioning

confidence: 99%

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

Brambilla

Mascetti

Mauri

2017

2017 IEEE International Conference on Big Data (Big Data)

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Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage "better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

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“…Grip Force Information. Previous study shows that the characteristic of variation in steering grip force is due to fatigue or loosing alertness [7][8]14]. Experiments with grip sensors (TekScan Grip sensor 4256E) attached to the hands (shown in Fig.…”

Section: ) Openness Definitionmentioning

confidence: 99%

Driver Fatigue Monitoring System Using Video Images and Steering Grip Force

Zhao¹,

Hao²,

Ding³

2016

Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)

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Previous research uses eye blink sensor technology to detect driver's fatigue, which has some limitations in the condition of wearing glasses and lighting changes. To overcome those problems, this paper proposes a way to detect fatigue for drivers through video images and grip forces on steering wheel. Simulated driving experiments are conducted on a platform developed by simulated driving software, during which grip forces of both hands and video images are collected. Analyzing the images from the video, we applied adaptive boosting (Adaboost) and Active Shape Models (ASM) algorithm to get the changes on the face, such as degrees of eye closure and degrees of mouth opening. These parameters including grip force that are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The results show that use of multiple visual parameters combined with steering grip force can effectively detect the driver's fatigue.

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

Cited by 21 publications

References 25 publications

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

Driver Fatigue Monitoring System Using Video Images and Steering Grip Force

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