Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa-1 with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion

BACKGROUND

Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns.

METHODS

We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021.

RESULTS

Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively.

CONCLUSIONS

Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.

Defining text neck: a scoping review

BACKGROUND

Text neck is regarded as a global epidemic. Yet, there is a lack of consensus concerning the definitions of text neck which challenges researchers and clinicians alike.

PURPOSE

To investigate how text neck is defined in peer-reviewed articles.

METHODS

We conducted a scoping review to identify all articles using the terms "text neck" or "tech neck." Embase, Medline, CINAHL, PubMed and Web of Science were searched from inception to 30 April 2022. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMAScR) guidelines. No limitation was applied for language or study design. Data extraction included study characteristics and the primary outcome relating to text neck definitions.

RESULTS

Forty-one articles were included. Text neck definitions varied across studies. The most frequent components of definitions were grouped into five basis for definition: Posture (n = 38; 92.7%), with qualifying adjectives meaning incorrect posture (n = 23; 56.1%) and posture without a qualifying adjective (n = 15; 36.6%); Overuse (n = 26; 63.4%); Mechanical stress or tensions (n = 17; 41.4%); Musculoskeletal symptoms (n = 15; 36.6%) and; Tissue damage (n = 7; 17.1%).

CONCLUSION

This study showed that posture is the defining characteristic of text neck in the academic literature. For research purposes, it seems that text neck is a habit of texting on the smartphone in a flexed neck position. Since there is no scientific evidence linking text neck with neck pain regardless of the definition used, adjectives like inappropriate or incorrect should be avoided when intended to qualify posture.

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.

Smartphone-Based High-Throughput Fluorimetric Assay for Histidine Quantification in Human Urine Using 96-Well Plates

A high-throughput fluorimetric assay for histidine was developed, using a 96-well plates platform. The analyte reacts selectively with o-phthalaldehyde under mild alkaline conditions to form a stable derivative. Instrumental-free detection was carried out using a smartphone after illumination under UV light (365 nm). The method was proved to be linear up to 100 μM histidine, with an LLOQ (lower limit of quantification) of 10 μM. The assay was only prone to interference from glutathione and histamine that exist in the urine samples at levels that are orders of magnitude lower compared to histidine. Human urine samples were analyzed following minimum treatment and were found to contain histidine in the range of 280 to 1540 μM. The results were in good agreement with an HPLC corroborative method.

DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation. Thus, there are challenges to meeting the requirements of real-time driver drowsiness detection applications that need short latency and lightweight computation. To this end, we applied Tiny Machine Learning (TinyML) to a driver drowsiness detection case study. In this paper, we first present an overview of TinyML. After conducting some preliminary experiments, we proposed five lightweight DL models that can be deployed on a microcontroller. We applied three DL models: SqueezeNet, AlexNet, and CNN. In addition, we adopted two pretrained models (MobileNet-V2 and MobileNet-V3) to find the best model in terms of size and accuracy results. After that, we applied the optimization methods to DL models using quantization. Three quantization methods were applied: quantization-aware training (QAT), full-integer quantization (FIQ), and dynamic range quantization (DRQ). The obtained results in terms of the model size show that the CNN model achieved the smallest size of 0.05 MB using the DRQ method, followed by SqueezeNet, AlexNet MobileNet-V3, and MobileNet-V2, with 0.141 MB, 0.58 MB, 1.16 MB, and 1.55 MB, respectively. The result after applying the optimization method was 0.9964 accuracy using DRQ in the MobileNet-V2 model, which outperformed the other models, followed by the SqueezeNet and AlexNet models, with 0.9951 and 0.9924 accuracies, respectively, using DRQ.

Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors

During the process of daily training or competition, athletes may suffer the situation that the load exceeds the body's bearing capacity, which makes the body's physiological function temporarily decline. It is one of the characteristics of sports fatigue. Continuous sports fatigue may incur permanent damage to the athletes if they cannot timely get enough rest to recover. In order to solve this issue and improve the quality of athlete's daily training, this paper establish a fatigue monitoring system by using multi-source sensors. First, the sEMG signals of athlete are collected by multi-source sensors which are installed in a wearable device. Second, the collected sEMG signals are segmented by using fixed window to be converted as Mel-frequency cepstral coefficients (MFCCs). Third, the MFCC features are used learn a Gaussian processing model which is used to monitor future muscle fatigue status. The experiments show that the proposed system can recognize more than 90% muscle fatigue states.

Risk of motorcycle collisions among patients with type 2 diabetes: a population-based cohort study with age and sex stratifications in Taiwan

AIMS

To investigate the overall and sex-age-specific absolute and relative risks of motorcycle collisions at road traffic accidents among patients with type 2 diabetes.

METHODS

A cohort study in Taiwan was conducted by following 989,495 patients with type 2 diabetes and the same number of matched controls recruited between 2010 and 2012 to the end of 2016. Collision events by motorcycle driver victims were identified from the Police-reported Traffic Accident Registry. Overall and sex-age-specific incidence rates of collision involving motorcycle driver victims were estimated under Poisson assumption. The Cox proportional hazard regression models were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of collision in association with type 2 diabetes.

RESULTS

Over an up to 7 years of follow-up, patients with type 2 diabetes had a higher incidence rate of motorcycle collision than controls at 1.16 and 0.89 per 100 person-years, respectively, which represented a significantly elevated HR of 1.28 (95% CI 1.27-1.30) after adjusting for potential confounders including various diabetic complications. The elevated HR was similarly seen in both men and women patients, and was significantly decreasing with increasing age regardless of sex. Little evidence supported the dose-response relationship between duration of type 2 diabetes and motorcycle collision risk.

CONCLUSIONS

After adjustment for common diabetic complications and comorbidities that could impair driving performance, patients with type 2 diabetes still suffered from increased risk of motorcycle collisions, regardless of sex, but was more evident in younger than in older patients.

Correlation between Eye Movements and Asthenopia: A Prospective Observational Study

Purpose: To analyze the correlation between eye movements and asthenopia so as to explore the possibility of using eye-tracking techniques for objective assessment of asthenopia. Methods: This prospective observational study used the computer visual syndrome questionnaire to assess the severity of asthenopia in 93 enrolled college students (age 20−30) who complained about asthenopia. Binocular accommodation and eye movements during the reading task were also examined. The correlations between questionnaire score and accommodation examination results and eye movement parameters were analyzed. Differences in eye movement parameters between the first and last reading paragraphs were compared. The trends in eye movement changes over time were observed. Results: About 81.7% of the subjects suffered from computer visual syndrome. Computer visual syndrome questionnaire total score was positively correlated with positive relative accommodation (p < 0.05). In the first reading paragraph, double vision was positively correlated with unknown saccades (all p < 0.05). Difficulty focusing at close range was positively correlated with total fixation duration, total visit duration, and reading speed (all p < 0.05). Feeling that sight was worsening was positively correlated with regressive saccades (p < 0.05). However, visual impairment symptoms were not significantly correlated with any accommodative function. In a total 20 min reading, significantly reduced eye movement parameters were: total fixation duration, fixation count, total visit duration, visit count, fixation duration mean, and reading speed (all p < 0.01). The eye movement parameters that were significantly increased were: visit duration mean and unknown saccades (all p < 0.001). Conclusion: Eye tracking could be used as an effective assessment for asthenopia. Among the various eye movement parameters, a decrease in fixation duration and counts may be one of the potential indicators related to asthenopia.

A Review of Recent Developments in Driver Drowsiness Detection Systems

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

This paper presents a camera-based prototype sensor for detecting fatigue and drowsiness in drivers, which are common causes of road accidents. The evaluation of the detector operation involved eight professional truck drivers, who drove the truck simulator twice—i.e., when they were rested and drowsy. The Fatigue Symptoms Scales (FSS) questionnaire was used to assess subjectively perceived levels of fatigue, whereas the percentage of eye closure time (PERCLOS), eye closure duration (ECD), and frequency of eye closure (FEC) were selected as eye closure-associated fatigue indicators, determined from the images of drivers’ faces captured by the sensor. Three alternative models for subjective fatigue were used to analyse the relationship between the raw score of the FSS questionnaire, and the eye closure-associated indicators were estimated. The results revealed that, in relation to the subjective assessment of fatigue, PERCLOS is a significant predictor of the changes observed in individual subjects during the performance of tasks, while ECD reflects the individual differences in subjective fatigue occurred both between drivers and in individual drivers between the ‘rested’ and ‘drowsy’ experimental conditions well. No relationship between the FEC index and the FSS state scale was found.