Non-intrusive vehicle-based measurement system for drowsiness detection

The purpose of this study is for prototyping a non-intrusive vehicle-based measurement system for drowsiness detection. The vehicle-based measurement system aims to achieve the non-intrusive drowsiness detection. The non-intrusive vehicle-based measurement achieved by placing sensors on the steering rod, gas pedal, and brake pedal. Drowsiness can be detected by comparing the position of the steering angle to the desired target angular position, especially when the difference in value of both is greater. Some sensors have been tested to obtain the actual steering angle position. From the test results, sensors that meet the criteria of accuracy are MPU6050 and HMC5883L. Both sensors have been tested in the prototyping of a vehicle-based drowsiness detection system with sufficient results. Furthermore, the prototype of non-intrusive vehicle-based drowsiness detection system has been integrated with interesting driving simulation software. The result has been able to show the actual condition of the steering position, the gas pedal and the brake pedal precisely. Moreover, this prototype opens opportunities to support the study of drowsiness detection using vehicle-based driving simulator

Modern drowsiness detection techniques: a review

According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness

Assessment of Driver’s Drowsiness Based on Fractal Dimensional Analysis of Sitting and Back Pressure Measurements

The most effective way of preventing motor vehicle accidents caused by drowsy driving is through a better understanding of drowsiness itself. Prior research on the detection of symptoms of drowsy driving has offered insights on providing drivers with advance warning of an elevated risk of crash. The present study measured back and sitting pressures during a simulated driving task under both high and low arousal conditions. Fluctuation of time series of center of pressure (COP) movement of back and sitting pressure was observed to possess a fractal property. The fractal dimensions were calculated to compare the high and low arousal conditions. The results showed that under low arousal (the drowsiness state) the fractal dimension was significantly lower than what was calculated with high arousal. Accumulated drowsiness thus contributed to the loss of self-similarity and unpredictability of time series of back and sitting pressure measurement. Drowsiness further reduces the complexity of the posture control system as viewed from back and sitting pressure. Thus, fractal dimension is a necessary and sufficient condition of a decreased arousal level. It further is a necessary condition for detecting the interval or point in time with high risk of crash

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs

Desarrollo de aplicaciones en LabVIEW y Android para sensores inalámbricos con giróscopo y comunicación con un simulador de conducción basado en Unity

Este Trabajo de Fin de Grado se enmarca en la monitorización fisiológica mediante sensores del conductor, para la detección de posibles estados de fatiga o baja atención del mismo. Los sensores empleados durante el mismo pertenecen a la empresa Shimmer. Este trabajo se centra principalmente en el uso de uno de estos sensores (giróscopo) para medir la velocidad angular de giro del volante del coche. Bajo este objetivo principal, se llevaron a cabo desarrollos de aplicaciones en diversas tecnologías: una aplicación Android y una aplicación LabVIEW. Con ellas, partiendo de funciones básicas de recepción de datos y sincronización con todos los sensores, implementamos una serie de procesados sobre la señal del giróscopo, tomando como referencia varios artículos científicos. Por otra parte, se ha desarrollado la comunicación y sincronización de la aplicación Android con un simulador de conducción desarrollado en Unity para la recogida de datos del conductor.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació