Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (&gt 92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human&ndash machine interaction in a car and especially for driver state monitoring in the field of automated driving. Document type: Articl

Wearables, IoT, and Big Data: The new revolution in cognitive science

A new revolution in cognitive science is now possible thanks to portable devices enabled to measure physiological variables non-intrusively, the Internet of Things that allows information to be collected and stored in real time from different locations, and big data techniques for identifying patterns that can be used to make decisions, predict behavior or create machine learning and artificial intelligence models. Research supported by these technologies will provide valuable insights into the impact that environmental circumstances have on the cognitive processes involved in different tasks, and how this can be detected through biological markers

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

Detecting driver fatigue using heart rate variability: A systematic review

Driver fatigue detection systems have potential to improve road safety by preventing crashes and saving lives. Conventional driver monitoring systems based on driving performance and facial features may be challenged by the application of automated driving systems. This limitation could potentially be overcome by monitoring systems based on physiological measurements. Heart rate variability (HRV) is a physiological marker of interest for detecting driver fatigue that can be measured during real life driving. This systematic review investigates the relationship between HRV measures and driver fatigue, as well as the performance of HRV based fatigue detection systems. With the applied eligibility criteria, 18 articles were identified in this review. Inconsistent results can be found within the studies that investigated differences of HRV measures between alert and fatigued drivers. For studies that developed HRV based fatigue detection systems, the detection performance showed a large variation, where the detection accuracy ranged from 44% to 100%. The inconsistency and variation of the results can be caused by differences in several key aspects in the study designs. Progress in this field is needed to determine the relationship between HRV and different fatigue causal factors and its connection to driver performance. To be deployed, HRV-based fatigue detection systems need to be thoroughly tested in real life conditions with good coverage of relevant driving scenarios and a sufficient number of participants

Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.Comment: 26 pages, 13 figures, 4 table

A systematic review of physiological signals based driver drowsiness detection systems.

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals. [Abstract copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving