Determining mechanical and electromyographical reaction time in a BCI driving fatigue experiment

Tan, Zong Xuan; Foong, Ruyi; Ang, Kai Keng

doi:10.1109/icics.2015.7459814

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studies reveal that there is a link between EMG amplitude and muscle fatigue as the amplitude of EMG signals decreases gradually with fatigue. The analysis of EMG data provided in [90,91,92,93,94] establishes a correlation between muscular fatigue and drowsiness. During muscle contraction, a shift in center frequency component is observed towards the lower spectral band [95,96].…”

Section: Driver-focused Studies and Systemsmentioning

confidence: 99%

A Comprehensive Survey of Driving Monitoring and Assistance Systems

Khan

Lee

2019

Sensors

163

View full text Add to dashboard Cite

Improving a vehicle driver’s performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience. The studies focused on the three main elements of the driving process, viz. driver, vehicle, and driving environment are analytically reviewed in this work, and a comprehensive framework of DMAS, major research areas, and their interaction is explored. A well-designed DMAS improves the driving experience by continuously monitoring the critical parameters associated with the driver, vehicle, and surroundings by acquiring and processing the data obtained from multiple sensors. A discussion on the challenges associated with the current and future DMAS and their potential solutions is also presented.

show abstract

Section: Driver-focused Studies and Systemsmentioning

confidence: 99%

A Comprehensive Survey of Driving Monitoring and Assistance Systems

Khan

Lee

2019

Sensors

163

View full text Add to dashboard Cite

show abstract

“…Biosignal data-based methods acquire data through wearable devices. Sensors such as electrocardiograms (ECG) [18][19][20], electromyography (EMG) [21][22][23], and electroencephalography (EEG) [24][25][26] are mainly used. Studies that use biosignal data directly represent the body state of the driver, and they also show excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Jeon

Kim²,

Baek

2021

Sensors

View full text Add to dashboard Cite

Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.

show abstract

“…It could be either labeled by raters via observations (Chai et al, 2017) or labeled by scales (Li & Chung, 2015), such as the Wierwille scale (Wierwille & Ellsworth, 1994). It could also be labeled based on driving performance such as reaction time to driving simulation events (Lin et al, 2012;Tan et al, 2015) or lane variability in the driving simulation (Lin et al, 2005a). A third way of labeling is to use the time-on-task factor, whereby the middle or end of the driving simulation is considered fatigued (Zhao et al, 2011b).…”

Section: Challenges Of Fatigue Detection In Drowsy Drivingmentioning

confidence: 99%

“…In the drowsy driving context, the fatigued data is the class of interest and is therefore denoted as the positive class, while alert data is then the negative class. To label alert (negative class) and fatigued (positive class) data from the driving data, many studies have used ratings (Chai et al, 2017;Li & Chung, 2015), driving performance (Lin et al, 2005a;Lin et al, 2012;Tan et al, 2015) or the time-on-task factor (Zhao et al, 2011b). The supervised learning approach is then utilized.…”

Section: Negative-unlabeled Learning In the Drowsy Driving Contextmentioning

confidence: 99%

Detecting passive fatigue with brain-computer interface in drowsy driving and stroke rehabilitation

Foong¹

Self Cite

View full text Add to dashboard Cite

Determining mechanical and electromyographical reaction time in a BCI driving fatigue experiment

Cited by 5 publications

References 16 publications

A Comprehensive Survey of Driving Monitoring and Assistance Systems

A Comprehensive Survey of Driving Monitoring and Assistance Systems

Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Detecting passive fatigue with brain-computer interface in drowsy driving and stroke rehabilitation

Contact Info

Product

Resources

About