Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition

Solaz, José; Laparra-Hernández, José; Bande, Daniel; Rodríguez, Noelia; Veleff, Sergio; Gerpe, José; Medina, Enrique

doi:10.1016/j.trpro.2016.05.472

Cited by 54 publications

(41 citation statements)

References 20 publications

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“…In the work of Solaz et al, the driver's breathing rate was captured from real-time image recognition. Results show that the kind of clothes influences and reduces system performance [59]. As part of a research project funded by the European Union, bio-sensors were built into car seat fabrics and seatbelts to measure heart rate and respiration [60].…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

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

Kundinger

Sofra

Riener

2020

Sensors

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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.

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Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

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

Kundinger

Sofra

Riener

2020

Sensors

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“…Drowsiness is defined as the transition between the states of responsiveness and sleep, during which reaction times are reduced (US Dot National Highway Traffic Safety Administration, 2018 ). Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety, due to clear declines in attention, the recognition of dangerous drivers, and the diminished vehicle-handling abilities associated with drowsiness (Wang, 2011 ; Solaz et al, 2016 ). In addition, drowsiness-related accidents cost billions of US dollars and result in the loss of lives in industry, including transportation, manufacturing, mining, maritime, and aerospace sectors.…”

Section: Introductionmentioning

confidence: 99%

“…Key economic sectors, such as transportation, construction, security, and manufacturing, reported loss of productivity and lives due to drowsiness (Wang, 2011 ; Solaz et al, 2016 ). In the transportation sector, drowsiness-influenced road accidents represent social and economic problems worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…In the transportation sector, drowsiness-influenced road accidents represent social and economic problems worldwide. In the European Union (EU), 25% of road accidents have been associated with fatigue and drowsiness, compared with 40% of fatal accidents in the United States (US) (Solaz et al, 2016 ; Wei et al, 2018 ). According to a National Highway Traffic Safety Administration (NHTSA) report, ~83,000 road accidents reported annually in the US are caused by driver fatigue.…”

Section: Introductionmentioning

confidence: 99%

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A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

115

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Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ∼27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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“…Particularly, in driving situations, it was shown that respiratory frequency tends to decrease, while respiratory regularity tends to increase, in the drowsy state [4,5]. Thus, respiration information has been often used to assess drivers' drowsiness [6][7][8][9][10][11][12]. Respiration characteristics of a specific time range have been often estimated from the power spectrum of the respiration signal in the time range.…”

Section: Introductionmentioning

confidence: 99%

Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection

Kim

Shin

2019

Electronics

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This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness.

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Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition

Cited by 54 publications

References 20 publications

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

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

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection

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