Odhad únavy člověka: využitelnost systémů dopravy ve vnitřním prostředí

Fatigue monitoring is nowadays domain in traffic and transportation (e.g. system for driver's sleepness monitoring in cars or in trains). People working in offices are affected by fatigue too, but there is no general system that is able to monitor it. The fatigue in offices can cause decreasing work productivity or security risks in the industry. This review article compares the advantages and disadvantages of approaches used in traffic (e.g. an eye-movement tracking, driver activity) in internal environment (in buildings) with focus on people that work in offices with a computer. Because of the greater possibility of movement, it can not be enough. People are in offices longer than in cars and this causes that they are more affected by the quality of the internal environment. It should be useful to include this information in a system for fatigue monitoring. It can result in a system that is able to quantify fatigue level from both biological and environment variables.Sledování únavy člověka je dnes hlavně doménou dopravy (systémy pro sledování řidiče v moderních automobilech, systémy pro strojvedoucí, atd.). U lidí pracujících v kancelářích se únava prakticky nesleduje, přestože její vliv může mít negativní dopad nejen na kvalitu a produktivitu práce, ale v případě osob na velínech v průmyslu také možná bezpečností rizika. Tato rešeršní práce se zabývá možnostmi aplikace systémů pro monitoring únavy řidiče automobilu (např. z pohybu očí, aktivit při řízení) na osoby pracující v kancelářských prostorách. To se vzhledem k možnostem pohybu po kanceláři jeví jako nedostatečné. Protože člověk tráví v kanceláři typicky více času než v automobilu, ovlivňuje jej výrazněji vnitřní prostředí budov, které je vhodné do odhadu únavy také zahrnout. Výsledkem tak může být systém kvantifikující míru únavy zohledněním jak vnitřního prostředí, tak vybraných biologických signálů člověka snímaných na pracovním místě

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

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

Fatigue analysis and design of a motorcycle online driver measurement tool using real-time sensors

Work fatigue is an important aspect and is very influential in determining the level of accidents, especially motorbike accidents. According to WHO, almost 30% of all deaths due to road accidents involve two- and three-wheel­ed motorized vehicles, such as motorbikes, mopeds, scooters and electric bicycles (e-bikes), and the number continues to increase. Motor­cycles dominate road deaths in many low- and middle-income countries, where nine out of ten traffic accident deaths occur among motorcyclists, as in Indonesia. However, until now, in Indonesia, there has been no monitor­ing system capable of identifying fatigue in motorbike drivers in the transportation sector. This research aims to determine fatigue patterns based on driver working hours and create a sensor system to monitor fatigue measurements in real-time to reduce the number of accidents. The research began with processing questionnaire data with Pearson correlation, which showed a close relationship between driver fatigue and driving time and a close relationship between fatigue and increased heart rate and sweating levels. From calibration tests with an error of 3% and direct measurements of working conditions, it was found that two-wheeled vehicle driver fatigue occurs after 2-3 hours of work. With a measurement system using the Box Whiskers analysis method, respondents' working conditions can also be de­ter­mined, which are divided into 4 zones, namely zone 1 (initial condition or good condition), zone 2 a declining condition, zone 3 a tired condition and zone 4 is a resting condition. Hopefully, this research will identify fati­gue zones correctly and reduce the number of accidents because it can iden­tify tired drivers so they do not have to force themselves to continue working and driving their motorbikes. As a conclusion from this research, a measure­ment system using two sensors, such as ECG and GSR can identify work fatigue zones well and is expected to reduce the number of accidents due to work fatigue.Pentingnya aspek identifikasi kondisi kelelahan kerja sangat mempengaruhi tingkat kecelakaan khususnya pada kecelakaan sepeda motor. Namun hingga saat ini di Indonesia belum ada sistem pemantauan yang mampu mengidentifikasi kelelahan pengemudi kendaraan sepeda motor di sektor transportasi. Tujuan dari penelitian ini adalah untuk mengetahui pola kelelahan berdasarkan jam kerja pengemudi dan membuat sistem sensor untuk memantau pengukuran kelelahan secara real-time untuk mengurangi angka kecelakaan. Hasil penelitian dari pengolahan data kuesioner dengan korelasi Pearson menunjukkan adanya hubungan yang erat, antara kelelahan dengan lama berkendara dan kelelahan yang erat kaitannya dengan peningkatan denyut nadi dan berkeringat. Dari pengujian kalibrasi dengan error 3% dan pengukuran langsung kondisi kerja, diperoleh kelelahan yang terjadi setelah 2-3 jam kerja. Dengan sistem pengukuran menggunakan metode analisa Box Whiskers ini juga dapat diketahui kondisi kerja responden yang terbagi menjadi 4 zona yaitu zona 1 (kondisi awal atau kondisi fit), zona 2 kondisi menurun, zona 3 kondisi lelah dan zona olah raga. 4 keadaan istirahat. Dari penelitian ini diharapkan dapat mengidentifikasi zona kelelahan dengan tepat dan dapat menurunkan angka kecelakaan karena dapat mengidentifikasi pengemudi yang kelelahan sehingga tidak memaksakan diri untuk terus bekerja dan mengemudikan sepeda motornya. Sebagai kesimpulan dari penelitian ini, sistem pengukuran menggunakan dua sensor seperti ECG dan GSR dapat mengidentifikasi zona kelelahan dengan baik dan diharapkan dapat mengurangi angka kecelakaan akibat kelelahan

Driving Monitoring System Application With Stretchable Conductive Inks: A Review

Nowadays the automotive industry is moving towards developing system connected vehicle parameters which can monitor the driver’s behaviour before driving. Most drivers lose focus and are emotionally distracted while driving owing to fatigue, drowsiness and alcohol consumption, that can result in a traffic accidents. The device or equipment used to detect the driver’s health before driving has always posed a problem in terms of the efficiency of the system especially concerning the cable connecting the equipment. Stretchable conductive ink (SCI) via electronic devices have been widely applied in various industries such as fabric, health, automotive, communications, etc. The flexibility allows a circuit to be placed on an uneven or constantly changing surface. However, till to-date, the effective use of the stretchable conductive ink has yet to be proven in the automotive industry. The current driver monitoring system cannot integrate with many of the driver's health level tracking features at one time. A combination of the driver’s monitoring system methods with stretchable conductive ink (SCI) sensors layout design can be used to prevent road accidents as a result of a driver’s behavior and will make the driving monitoring system more effective with soft substrates technology that has the advantage of geometric deformation based on appropriate shapes

Smart Safety and Accident Prevention System

The primary cause of road accident results in fatalities, serious injuries and monetary losses is known to be due to drowsy or sleepy drivers, according to analysis reports on recent traffic accidents. Lack of sleep, medication, drugs, or prolonged driving contributes to drowsiness. A system that can identify a driver’s drowsy state and warn him before an accident occurs is required to avoid roadside accidents caused by distracted driving. Many researchers have recently expressed their interest in drowsiness detection. The methods essentially involve monitoring the driver’s physiological or behavioral 1summarizes some of the most recent methods put forth in this field is given. We propose an algorithm to monitor eye blinks that uses eye feature points to determine whether the eye is open or closed and sets off an alarm if the driver is drowsy. In-depth experimental results are also provided to highlight the benefits and drawbacks of the proposed method

Driver-centered pervasive application for heart rate measurement

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology

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

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios