Match!

Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: a neural network approach

Published on Dec 14, 2009
· DOI :10.1145/1806338.1806478
Mahesh M. Bundele4
Estimated H-index: 4
,
Rahul Banerjee7
Estimated H-index: 7
(BITS: Birla Institute of Technology and Science)
Abstract
Vehicular accidents are increasingly contributing towards loss of lives across the world. Timely detection of physiological and psychological parameters of the vehicular driver, which could cause various levels of physical and mental fatigue that lead to slower reflexes is therefore extremely important. As part of an ambitious research initiative, India is developing a pervasive computing solution for eliminating / reducing such accidents. As one of the component of such solution, a wearable computing system has been envisioned to be worn by the driver. A complex set of noninvasive and nonintrusive sensor-compute element integrated with appropriate e-textile would form the primary part of this wearable computer. Out of the initial set of physiological parameters such as Skin Conductance, Oximetry Pulse, Respiration, SPO2, the current work focuses on the first two parameters to detect and monitor the mental fatigue / drowsiness of a driver. Using Neural Network approach, Multilayer Perceptron Neural Networks (MLP NN) have been designed to classify Pre and Posting driving fatigue levels. The performance of single hidden layer and two hidden layers based MLP NN have been discussed using the performance measures such as, Percentage Classification Accuracy (PCLA), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Area under Receiver Operating Characteristic Curve (AROC), Area under Convex Hull of ROC (AHROC). It was discovered that the performance of one hidden layer based MLP NN is comparable to the two hidden layers based MLP NN and there is slight rise in PCLA from One hidden layer to two hidden layer.
  • References (15)
  • Citations (35)
📖 Papers frequently viewed together
428 Citations
227 Citations
5 Citations
78% of Scinapse members use related papers. After signing in, all features are FREE.
References15
Newest
#1Shuyan Hu (Beihang University)H-Index: 1
#2Gangtie Zheng (Beihang University)H-Index: 1
Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness level...
142 CitationsSource
We have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from w...
55 CitationsSource
#1Mervyn V. M. Yeo (NUS: National University of Singapore)H-Index: 2
#2Xiansheng Li (NUS: National University of Singapore)H-Index: 3
Last. Einar Wilder-Smith (NUS: National University of Singapore)H-Index: 27
view all 4 authors...
This study aims to develop an automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents a superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states was tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by ...
173 CitationsSource
#1S.P. LinderH-Index: 10
#2Suzanne M. Wendelken (Dartmouth College)H-Index: 11
Last. Paul R. Steiner (Dartmouth–Hitchcock Medical Center)H-Index: 2
view all 4 authors...
Exercise induced hemodynamic stress has been studied extensively using a wide range of physiological sensors. While athletes can modulate their training intensity using EKG-based heart rate monitors, there are currently no noninvasive monitors that can be used to ascertain with a high degree of certainty the hemodynamic stress an individual is experiencing because of fatigue or an underlying pathology. We propose that cardiac stress will result in detectable changes in skin blood flow. In a clin...
5 CitationsSource
#1Nikhil R. Pal (NCTU: National Chiao Tung University)H-Index: 49
#2Chien-Yao Chuang (NCTU: National Chiao Tung University)H-Index: 2
Last. Chin-Teng Lin (NCTU: National Chiao Tung University)H-Index: 57
view all 7 authors...
Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states...
66 CitationsSource
#1N.N. CharniyaH-Index: 1
#2S.V. Dudul (Vishwakarma Institute of Information Technology)H-Index: 1
This paper presents a novel sensor for classification of material type and its surface roughness. The sensor is developed by means of a lightweight plunger probe and an optical mouse. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The time and features of bouncing signal are related to the material type and its surface properties, and each material has a unique set of such propertie...
9 CitationsSource
#1Nuria Oliver (Microsoft)H-Index: 41
#2Fernando Flores-Mangas (U of T: University of Toronto)H-Index: 5
We present HealthGear, a real-time wearable system for monitoring, visualizing and analyzing physiological signals. HealthGear consists of a set of non-invasive physiological sensors wirelessly connected via Bluetooth to a cell phone which stores, transmits and analyzes the physiological data, and presents it to the user in an intelligible way. In this paper, we focus on an implementation of HealthGear using a blood oximeter to monitor the user’s blood oxygen level and pulse while sleeping. We a...
53 CitationsSource
Aug 1, 2006 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1S.P. Linder (MIT: Massachusetts Institute of Technology)H-Index: 10
#2Suzanne M. Wendelken (Dartmouth College)H-Index: 11
Last. Jeffrey ClaymanH-Index: 2
view all 3 authors...
The effect of exercise on the cardiovascular system has been studied extensively using a wide range of physiological sensors. Athletes now commonly use EKG-based monitors to ascertain heart rate, but these devices cannot directly monitor the level of physical stress. We hypothesize that the low frequency spindle waves seen in the photoplethysmographs (PPG) of exercising individuals may be useful for noninvasively detecting hemodynamic stressors to the human vascular system. In a clinical trial w...
6 CitationsSource
#1Rahul Banerjee (BITS: Birla Institute of Technology and Science)H-Index: 7
In a nutshell, the course is basically about emerging, visible and invisible computing systems and devices. Pervasive computing has many names, including ubiquitous computing, and its key element is the omnipresence of information devices. These devices can be embedded into cars, airplanes, ships, bikes, posters, signboards, walls, and even clothes. The course therefore focuses on independent information devices, including wearable computers, mobile phones, screen phones, and PDAs, and the servi...
10 CitationsSource
#1Abdulhamit Subasi (Kahramanmaraş Sütçü İmam University)H-Index: 30
Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. This paper deals with a novel method of analysis of EEG signals using wavelet transform, and classification using ANN. EEG signals were decomposed into ...
200 CitationsSource
Cited By35
Newest
#2Mahesh M. BundeleH-Index: 4
In vehicular drivers, the cognitive fatigue has been one of the major factors that leads to loss of lives or disabilities due to vehicular accidents (Bundele and Banerjee in Proceedings of the 11th international conference on information integration and web-based applications and services, ACM, pp 739–744, 2009 [1]). The factors governing driver fatigue such as monotonous driving, traffic conditions on road, road conditions, insufficient sleep, anxiety, health conditions, work environment, type ...
Source
#1V Mekaladevi (Amrita Vishwa Vidyapeetham)
#2N. Mohankumar (Amrita Vishwa Vidyapeetham)
Driving is an activity in which all the senses have to act together and a small pain may lead to major accidents. Health monitoring system helps people suffering from chronic diseases and who needs periodic and timely medical attention. The number of deaths due to road traffic accidents has been reduced, which shows that the inventions to increase road safety have some impact. The driver's health condition is being monitored by an inbuilt nonintrusive measurement system which assures the safety ...
Source
#1Tobias Mettler (UNIL: University of Lausanne)H-Index: 15
#2Jochen Wulf (HSG: University of St. Gallen)H-Index: 8
Wearables paired with data analytics and machine learning algorithms that measure physiological (and other) parameters are slowly finding their way into our workplace. Several studies have reported positive effects from using such “physiolytics” devices and purported the notion that it may lead to significant workplace safety improvements or to increased awareness among employees concerning unhealthy work practices and other job‐related health and well‐being issues. At the same time, physiolytic...
13 CitationsSource
#1Ronnie S. ConcepcionII (University of Perpetual Help System DALTA)
#2Jommel S. Manalo (University of Perpetual Help System DALTA)
Last. Lorena C. Ilagan (University of Perpetual Help System DALTA)
view all 8 authors...
Preempting mental fatigue may cause decrease in the quality of life and the worst accidents. A system of electrocardiography and electromyography signals can enhance the detection of alertness and mental fatigue. This study determines the suitability of some computational intelligence, namely, artificial neural network (ANN), fuzzy logic system, and a Sugeno adaptive neuro-fuzzy inference system (ANFIS), in detecting mental alertness and fatigue of a person using neurophysiological signals of el...
2 CitationsSource
#1Yuhao Wang (PolyU: Hong Kong Polytechnic University)
#2Ivan Wang-Hei Ho (PolyU: Hong Kong Polytechnic University)H-Index: 13
Google defines the concept of autonomous driving as one of the applications of big data. Specifically, with the input sensor data, the autonomous vehicles can be provided with the semantic-level driving characteristics for an accurate and safe driving control. However, both the enumeration of handcrafted driving features with expert knowledge and the feature classification with machine learning for characterizing driving behaviors is lack of practicability under a complex scale. Therefore, this ...
1 CitationsSource
#1Charlotte Jacobé de Naurois (AMU: Aix-Marseille University)H-Index: 2
#2Christophe Bourdin (AMU: Aix-Marseille University)H-Index: 17
Last. Jean-Louis Vercher (AMU: Aix-Marseille University)H-Index: 7
view all 5 authors...
Abstract Not just detecting but also predicting impairment of a car driver’s operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110 min un...
11 CitationsSource
#2Yong WangH-Index: 33
Last. Ming Yang
view all 6 authors...
Driving fatigue becomes the main factor causing vehicular accidents. Detecting driving fatigue using physiological sensors is considered an effective method. In this paper, we develop a driving fatigue detection system called "SYMAGIC smart ring", which can be applied in the real industries. It includes three parts: a hardware device, a phone application and a connection to the cloud server. The whole system is economical and robust, which meets the requirements of the transportation industries.
2 CitationsSource
#1Jibo He (WSU: Wichita State University)H-Index: 13
#2William Choi (WSU: Wichita State University)H-Index: 3
Last. Kaiping Peng (THU: Tsinghua University)H-Index: 14
view all 6 authors...
Abstract Background Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated. Methods The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compa...
7 CitationsSource
#1Boon-Giin Lee (KMU: Keimyung University)H-Index: 11
#2Teak Wei Chong (KMU: Keimyung University)H-Index: 3
Last. Beom-Joon Kim (KMU: Keimyung University)H-Index: 6
view all 6 authors...
Negative emotional responses are a growing problem among drivers, particularly in countries with heavy traffic, and may lead to serious accidents on the road. Measuring stress- and fatigue-induced emotional responses by means of a wireless, wearable system would be useful for potentially averting roadway tragedies. The focus of this study was to develop and verify an emotional response-monitoring paradigm for drivers, derived from electromyography signals of the upper trapezius muscle, photoplet...
6 CitationsSource
Jul 1, 2017 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Anwesha Sengupta (IIT-KGP: Indian Institute of Technology Kharagpur)H-Index: 2
#2Abhishek Tiwari (Bosch)H-Index: 1
Last. Aurobinda Routray (IIT-KGP: Indian Institute of Technology Kharagpur)H-Index: 24
view all 3 authors...
Continuous and repetitive performance of a task is likely to induce a drop in alertness levels of an individual. Changes in Electroencephalogram (EEG) have been proposed in literature as a marker of alertness during repeated performance of a cognitive task set. The present paper investigates the increase in fatigue levels and resultant drop in alertness of subjects during continuous performance of cognitive tasks by analyzing changes in energy of EEG frequency bands. The trends reflected in the ...
3 CitationsSource