Using Machine Learning to Determine the Motorist Somnolence

Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organ-ization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection sys-tem that can effectively minimize accidents associated with exhaustion. The system uti-lizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined thresh-old, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi fa-cilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to miti-gate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel

Long driving hours and health of truck drivers

In recent time, the health of truck drivers has become a concern for regulatory agencies and safety professionals all over the world. Fatal and non-fatal injury rates for truck drivers are among the highest of all occupations. Truck driving is an important and tedious job. Driving for long hours, drivers are confined to a small space, sit with static lower and upper extremities posture, mentally focus and absorb vibrations. This thesis provides an in-depth review of the literature related to the problems of long distance truck drivers and commercial motor vehicle operators. The Literature suggests that continuous exposure of truck drivers to risk factors has led to such illnesses as musculoskeletal disorders, obesity, hypertension, cardiovascular disease, stroke, sleep disorders and psychological distress. Prolonged sitting, whole-body vibration, physical and psychological fatigues were found to be the main risk factors that are related to the occupational health problems of truck drivers. These occupational risk factors were analyzed in detail to understand the physiological pathways that cause the risk factors to affect truck drivers\u27 health. Based on these analyses, a set of suggestions on continuous improvement was made in areas of rest break, physical exercise, health monitoring, and psychological well being

Monitoring fatigue and drowsiness in motor vehicle occupants using electrocardiogram and heart rate - A systematic review

Introdução: A fadiga é um estado complexo que pode resultar em diminuição da vigilância, frequentemente acompanhada de sonolência. A fadiga durante a condução contribui significativamente para acidentes de trânsito em todo o mundo, destacando-se a necessidade de técnicas de monitorização eficazes. Existem várias tecnologias para aumentar a segurança do condutor e reduzir os riscos de acidentes, como sistemas de deteção de fadiga que podem alertar os condutores à medida que a sonolência se instala. Em particular, a análise dos padrões de frequência cardíaca pode oferecer informações valiosas sobre a condição fisiológica e o nível de vigilância do condutor, permitindo-lhe compreender os seus níveis de fadiga. Esta revisão tem como objetivo estabelecer o estado atual das estratégias de monitorização para ocupantes de veículos, com foco específico na avaliação da fadiga pela frequência cardíaca e variabilidade da frequência cardíaca. Métodos: Realizamos uma pesquisa sistemática da literatura nas bases de dados Web of Science, SCOPUS e Pubmed, utilizando os termos veículo, condutor, monitoração fisiológica, fadiga, sono, eletrocardiograma, frequência cardíaca e variabilidade da frequência cardíaca. Examinamos artigos publicados entre 1 de janeiro de 2018 e 31 de janeiro de 2023. Resultados: Um total de 371 artigos foram identificados, dos quais 71 foram incluídos neste estudo. Entre os artigos incluídos, 57 utilizam o eletrocardiograma (ECG) como sinal adquirido para medir a frequência cardíaca, sendo que a maioria das leituras de ECG foi obtida através de sensores de contacto (n=41), seguidos por sensores vestíveis não invasivos (n=11). Relativamente à validação, 23 artigos não mencionam qualquer tipo de validação, enquanto a maioria se baseia em avaliações subjetivas de fadiga relatadas pelos próprios participantes (n=27) e avaliações feitas por observadores com base em vídeos (n=11). Dos artigos incluídos, apenas 14 englobam um sistema de estimativa de fadiga e sonolência. Alguns relatam um desempenho satisfatórios, no entanto, o tamanho reduzido da amostra limita a abrangência de quaisquer conclusões. Conclusão: Esta revisão destaca o potencial da análise da frequência cardíaca e da instrumentação não invasiva para a monitorização contínua do estado do condutor e deteção de sonolência. Uma das principais questões é a falta de métodos suficientes de validação e estimativa para a fadiga, o que contribui para a insuficiência dos métodos na criação de sistemas de alarme proativos. Esta área apresenta grandes perspetivas, mas ainda está longe de ser implementada de forma fiável.Background: Fatigue is a complex state that can result in decreased alertness, often accompanied by drowsiness. Driving fatigue has become a significant contributor to traffic accidents globally, highlighting the need for effective monitoring techniques. Various technologies exist to enhance driver safety and minimize accident risks, such as fatigue detection systems that can alert drivers as drowsiness sets in. In particular, measuring heart rate patterns may offer valuable insights into the occupant's physiological condition and level of alertness, and may allow them to understand their fatigue levels. This review aims to establish the current state of the art of monitoring strategies for vehicle occupants, specifically focusing on fatigue assessed by heart rate and heart rate variability. Methods: We performed a systematic literature search in the databases of Web Of Science, SCOPUS and Pubmed, using the terms vehicle, driver, physiologic monitoring, fatigue, sleep, electrocardiogram, heart rate and heart rate variability. We examine articles published between 1st of january 2018 and 31st of January 2023. Results: A total of 371 papers were identified from which 71 articles were included in this study. Among the included papers, 57 utilized electrocardiogram (ECG) as the acquired signal for heart rate (HR) measures, with most ECG readings obtained through contact sensors (n=41), followed by non-intrusive wearable sensors (n=11). Regarding validation, 23 papers do not report validation, while the majority rely on subjective self-reported fatigue ratings (n=27) and video-based observer ratings(n=11). From the included papers, only 14 comprise a fatigue and drowsiness estimation system. Some report acceptable performances, but reduced sample size limits the reach of any conclusions. Conclusions: This review highlights the potential of HR analysis and non-intrusive instrumentation for continuous monitoring of driver's status and detecting sleepiness. One major issue is the lack of sufficient validation and estimation methods for fatigue, contributing to the insufficiency of methods in providing proactive alarm systems. This area shows great promise but is still far from being reliably implemented

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

The effects of different fatigue levels on brain–behavior relationships in driving

© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Background: In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain–behavior relationships. Methods: A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. Results: Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. Conclusion: Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators

A novel framework to promote eco-driving through smartphone-vehicle integration

Tesis por compendioIt was not that long ago, just in the first half on the 1990s, when mobile phones were first introduced, being big and expensive. All you could do with them was to make phone calls. Since then mobile devices have experienced a great technological advance: we carry smartphones in our pockets that provide Internet access, having accelerometers that can measure acceleration, a gyroscope that can provide orientation information, different wireless interfaces such as Bluetooth connections, and above all, great computing power. On the other hand, the automobile industry has evolved significantly during the last 10 years. One of the most exciting advances in vehicle development is vehicle-to-vehicle V2V communication, which allows cars to communicate with each other over a dedicated Wi-Fi band, and share information about vehicle speed, route direction, traffic flow, and road and weather conditions. An example of such a system is GM's (General Motors) OnStar, introduced in 1996, and that provides automatic response in case of an accident, stolen-vehicle recovery, remote door unlock, and vehicle diagnostics. Also, the standard On Board Diagnosis (OBD-II), available for several years, allows us to connect to the Electronic Control Unit (ECU) via a Bluetooth OBD-II connector. This connection interface allows connectivity between the smartphone and the vehicle, and can be purchased for just over 15 euros. The spectrum of possibilities that arise when combining the car and the smartphone is unlimited, such as performing the diagnosis of the car by assuming the tasks performed by the car's On Board Unit (OBU), or sending the collected data to a platform where the diagnosis or maintenance of the system can be realized in order to detect possible faults, help you to save gas and reduce environment pollution, and notify you of your car's problems, among other features. The general objective pursued with this doctoral thesis is to help drivers to correct bad habits in their driving. To achieve this we promote the combination between smartphones and vehicular networks to design and develop a platform able to offer useful tips to achieve safer driving and greater fuel economy. It is well-known that intelligent driving can lead to lower fuel consumption, with the consequent positive impact on the environment. The proposal that has been carried out in this doctoral thesis begins with the data capture from the vehicles' OBD-II port and data analysis through the use of graphs, maps, and statistics, both, on the server itself and in the smartphone's application developed. We applied data mining techniques and neural networks to analyze, study and generate a classiffication on driving styles based on the analysis of the characteristics of each specific route used for testing. In a second phase, we demostrate the relationship between fuel consumption and driving style. To achieve that goal, the first thing that we had to realize was how to apply different algorithms for the instantaneous consumption calculation (this parameter cannot be obtained directly from the vehicle ECU). Later, we studied and analyzed all data that was collected from the drivers who shared their monitored data with the server. Although drivers do not recognize themselves as being in a state of anxiety while driving, they are more stressed than in any other daily activity, for example, when trying to stay in the right lane, keeping the car at a certain speed, and starting and stopping the vehicle. In general, drivers are more concentrated than they think, which causes an increase in the heart rate. Many factors influence heart rate while at rest, e.g. stress, medications, medical conditions, even genes play a role. In our study we also investigate how stress and the driving behavior influence the heart rate. So, in the last phase, we demostrate the correlation between heart rate and driving style, showing how the driving style can make the heart rate vary by 3 %.No hace mucho tiempo, tan sólo en la primera mitad en la década de los 90, cuando los teléfonos móviles aparecieron, eran grandes y caros, todo lo que se podía hacer con ellos era realizar llamadas telefónicas. Desde entonces los dispositivos móviles han experimentado un gran avance tecnológico, llevamos teléfonos inteligentes en el bolsillo con acceso a Internet, acelerómetros que calculan la aceleración instantánea, giroscopios que proporcionan información de orientación, diferentes conexiones inalámbricas como Bluetooth, y sobre todo, gran capacidad de computación. Por otro lado, la industria del automóvil ha evolucionado mucho durante los últimos 10 años. Uno de los avances más interesantes en el desarrollo de vehículos ha sido la conectividad, V2V, o comunicación vehículo a vehículo, permite a los automóviles comunicarse mediante Wi-Fi y compartir información sobre la velocidad del vehículo, la dirección de la ruta actual, el tráfico, así como las condiciones de la carretera y las condiciones ambientales. Por otra parte, el estándar On Board Diagnosis (OBD-II), disponible desde hace varios años, permite conectarnos de forma sencilla a la ECU (Electronic Control Unit) mediante un conector Bluetooth OBD-II. Este interfaz de conexión permite la conectividad entre el dispositivo móvil y el vehículo, se puede adquirir por poco más de 15 euros. El espectro de posibilidades que surgen al combinar el automóvil y el Smartphone es amplísimo, como por ejemplo realizar el diagnóstico del coche a través del móvil asumiendo las tareas que hace la unidad On Board Unit (OBU) del coche, o bien enviar los datos recogidos a una plataforma donde se pueda realizar el diagnóstico o mantenimiento del sistema, detectando posibles fallos puede ayudar a ahorrar en el consumo de combustible, notificar los problemas del coche en tiempo real, entre otras características. El objetivo general que se persigue con esta tesis doctoral es ayudar al conductor a corregir malos hábitos en su forma de conducción. Conseguimos esto mediante la combinación entre smartphones y las redes vehiculares, diseñamos y desarrollamos una plataforma capaz de ofrecer consejos útiles para conseguir una conducción más segura y un mayor ahorro de combustible. Es conocido que una conducción inteligente puede llevarnos a un menor consumo de combustible, con el consiguiente impacto positivo que ello conlleva sobre el medio ambiente. La propuesta que se ha llevado a cabo en esta tesis doctoral comienza con la obtención de los datos desde el OBD-II del coche y su presentación y análisis mediante el uso de gráficas, mapas, estadísticas, tanto en el propio servidor como en la aplicación móvil desarrollada para la obtención de datos recibidos desde la ECU. Se aplicaron técnicas de minería de datos y redes neuronales para analizar, estudiar y generar una clasificación sobre los estilos de conducción en base al análisis de las características de la vía sobre la que ha realizado la ruta. En una segunda fase se demostró la relación entre el consumo de combustible con el estilo de conducción, para ello lo primero que tuvimos que realizar fue aplicar diversos algoritmos para el cálculo del consumo instantáneo, este parámetro no es posible obtenerlo directamente de la ECU del vehículo. Posteriormente se realizó el estudio y el análisis de todos los datos que se recogieron de los conductores que se prestaron a la realización del estudio enviando los datos al servidor. Muchos factores influyen en la frecuencia cardíaca en reposo, por ejemplo, el estrés, los medicamentos, las condiciones médicas, incluso los genes tienen su influencia, el envejecimiento tiende a acelerarlo, y el ejercicio regular tiende a ralentizarlo. En nuestro estudio también investigamos cómo el estrés y el comportamiento en la conducción influyen en la frecuencia cardíaca. En la última fase vemos la correlación existente entre el riNo fa molt de temps, tan sols en la primera mitat en la dècada dels 90, quan els telèfons mòbils van aparéixer, eren grans i cars, tot el que es podia fer amb ells era realitzar telefonades. Des de llavors els dispositius mòbils han experimentat un gran avanç tecnològic, portem telèfons intel_ligents en la butxaca amb accés a Internet, acceleròmetres que calculen l'acceleració instantània, giroscopis que proporcionen informació d'orientació, diferents connexions sense _ls com Bluetooth, i sobretot gran capacitat de computació. D'altra banda, la indústria de l'automòbil ha evolucionat molt durant els últims 10 anys. Un dels avanços més interessants en el desenrotllament de vehicles ha sigut la connectivitat, V2V, o comunicació vehicle a vehicle, permet als automòbils comunicar-se per mitjà de la banda de Wi-Fi i compartir información sobre la velocitat del vehicle, la direcció de la ruta actual, les condicions del trà_c, així com l'estat de la carretera i les condicions ambientals. D'altra banda l'estàndard On Board Diagnosi (OBD-II), disponible des de fa diversos anys, permet connectar-nos de forma senzilla a l'ECU (Electronic Control Unit) per mitjà d'un connector Bluetooth OBD-II. Esta interfície de connexió permet la connectivitat entre el dispositiu mòbil i el vehicle, es pot adquirir per poc més de 15 euros. L'espectre de possibilitats que sorgixen al combinar l'automòbil i el Smartphone és il_limitat, com per exemple realitzar el diagnòstic del cotxe a través del móvil assumint les tasques que fa la unitat On Board Unit (OBU) del cotxe, o bé enviar les dades arreplegades a una plataforma on es puga realitzar el diagnòstic o manteniment del sistema, detectant possibles fallades, ajuda a estalviar en el consum de combustible, noti_car els problemes del cotxe en temps real, entre altres característiques. L'objectiu general que es perseguix amb esta tesi doctoral és ajudar al conductor a corregir mals hàbits en la seua forma de conducció. Aconseguim açò mitjançant de la combinació entre smartphones i les xarxes vehiculares, dissenyem i desenrotllem una plataforma capaç d'oferir consells útils per a aconseguir una conducció més segura i un major estalvi de combustible. És conegut que una conducció intel_ligent pot emportar-nos a un menor consum de combustible, amb el consegüent impacte positiu que això comporta sobre el medi ambient. La proposta que s'ha dut a terme en esta tesi doctoral comença amb l'obtenció de les dades des de l'OBD-II del cotxe i la seua presentació i anàlisi per mitjà de l'ús de grà_ques, mapes, estadístiques, tant en el propi servidor, com en l'aplicació mòbil desenrotllada per a l'obtenció de dades rebudes des de l'ECU. S'apliquen tècniques de mineria de dades i xarxes neuronals per a analitzar, estudiar i generar una classi_cació sobre els estils de conducció basant-se en l'anàlisi de les característiques de la via sobre la qual ha realitzat la ruta. En una segona fase es va a demostrar la relació entre el consum de combustible amb l'estil de conducció, per a això la primera cosa que vam haver de realizar va ser aplicar diversos algorismes per al càlcul del consum instantani, este paràmetre no és possible obtindre-ho directament de l'ECU del vehicle. Posteriorment es va realitzar l'estudi i l'anàlisi de totes les dades que es van arreplegar dels conductors que es van prestar a la realització de l'estudi enviant les dades al servidor. Molts factors in_ueixen en la freqüència cardíaca en repòs, per exemple, l'estrès, els medicaments, les condicions mèdiques, _ns i tot els gens tenen la seua in_uència, l'envelliment tendeix a accelerar-ho, i l'exercici regular tendeix a ralentir-ho. En el nostre estudi només estem interessats en com l'estrès i el comportament en la conducció in_ueixen en la freqüència cardíaca. En l'última fase vam veure la correlació existent entre el ritme cardíac i l'estil de conducciMeseguer Anastasio, JE. (2017). A novel framework to promote eco-driving through smartphone-vehicle integration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84287TESISCompendi

複素独立成分分析によるヒト脳波アルファ波のトポグラフ解析

Tohoku University中尾光

Factors associated with different levels of daytime sleepiness among Korean construction drivers: a cross-sectional study

Background: Commercial vehicle accidents are the leading cause of occupational fatalities and an increased risk of traffic accidents is associated with excessive fatigue, other health problems as well as poor sleep during work. This study explores individual and occupational factors associated with different levels of daytime sleepiness and identifies their association with driving risk among occupational drivers working at construction sites. Methods: This cross-sectional and correlational study adopted a self-reported questionnaire of Korean construction drivers (N = 492). The data were collected from October 2018 to February 2019 using a battery of six validated instruments about participants' sociodemographic, health-related, and occupational characteristics. One-way ANOVA and multinomial logistic regression were conducted using IBM SPSS WIN/VER 25.0, with a two-tailed alpha of .05. Results: Based on the Epworth Sleepiness Scale, "moderate" (31.7%) and "severe" (10.2%) daytime sleepiness groups were identified. There were significant differences in break time, driving fatigue, depressive symptom, subjective sleep quality, physical and mental health, and driving risk among the three groups (all p-values < .001). Driving fatigue (Adjusted Odds Ratio [aOR] = 1.08, 1.17), depressive symptoms (aOR = 0.91, 0.98), subjective sleep quality (aOR = 1.18 in moderate only), and driving over the speed limit (aOR = 1.43, 2.25) were significant factors for determining "moderate" and "severe" daytime sleepiness groups, respectively. Conclusion: A significant number of construction drivers experience excessive daytime sleepiness; thus it is important to reduce the negative impact of driving fatigue and other factors on daytime sleepiness. Our study findings suggest that occupational health care providers should pay attention to development and implementation of health management interventions to reduce driving fatigue that incorporate the drivers' physical, mental, and occupational factors. Professional organizations need to establish internal regulations and public policies to promote health and safety among occupational drivers who specifically work at construction sites.ope

Human Requirements Validation for Complex Systems Design

AbstractOne of the most critical phases in complex systems design is the requirements engineering process. During this phase, system designers need to accurately elicit, model and validate the desired system based on user requirements. Smart driver assistive technologies (SDAT) belong to a class of complex systems that are used to alleviate accident risk by improving situation awareness, reducing driver workload or enhancing driver attentiveness. Such systems aim to draw drivers’ attention on critical information cues that improve decision making. Discovering the requirements for such systems necessitates a holistic approach that addresses not only functional and non-functional aspects but also the human requirements such as drivers’ situation awareness and workload. This work describes a simulation-based user requirements discovery method. It utilizes the benefits of a modular virtual reality simulator to model driving conditions to discover user needs that subsequently inform the design of prototype SDATs that exploit the augmented reality method. Herein, we illustrate the development of the simulator, the elicitation of user needs through an experiment and the prototype SDAT designs using UNITY game engine