Drowsy driver mobile application: Development of a novel scleral-area detection method

Mohammad, Faisal; Mahadas, Kausalendra; Hung, George K.

doi:10.1016/j.compbiomed.2017.07.027

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…One direction involves making detectors by storing images with or without ureters and using machine learning (including deep learning). For example, we could make detectors using the traincascade algorithm with OpenCV . However, this method, in theory, requires thousands of images for learning and cannot be applied to unlearned cases.…”

Section: Discussionmentioning

confidence: 99%

Computer vision for total laparoscopic hysterectomy

Sato

Koizumi

Nakabayashi

et al. 2018

Asian J Endoscop Surgery

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Section: Discussionmentioning

confidence: 99%

Computer vision for total laparoscopic hysterectomy

Sato

Koizumi

Nakabayashi

et al. 2018

Asian J Endoscop Surgery

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“…The sleepiness detection system continues to be matured and also diverse. Such as an implementation of a webcam that is connected to a computer [13], Internet of Things (IoT) approach [18], [20], wearables [21] and even utilizing smartphones [9], [10], [22], [23]. Most of the researchers are competing to promote a method that is easy to perform and has a comprehensive impact.…”

Section: Related Workmentioning

confidence: 99%

“…Haar Cascade Classifier is the most common method for identifying eye area [10], [13], [15]. But some of them were applying the Histogram of Oriented Gradient (HOG).…”

Section: Related Workmentioning

confidence: 99%

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Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values

Huda

Tolle

Utaminingrum

2020

Int. J. Interact. Mob. Technol.

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Sleepiness during driving is a dangerous problem faced by all countries. Many studies have been conducted and stated that sleepiness threatens the driver himself and other peoples. The victim not only suffered minor injuries but also many of them ended in death. Nowadays, there are many kinds of studies to improve sleep detection methods. But it faces difficulties such as lack of accuracy, and poor performance of detection; thus the system inadequate works in real-time. Recently, automobile companies have begun manufacturing special equipment to recognize sleepiness driver. However, the technologies are only implemented in certain cars since the price is still quite expensive. Therefore, a system with a comprehensive method is needed to discover the driver's sleepiness accurately at an affordable price. This study proposed driver sleepiness detection implemented on a smartphone. The system is capable to identify closed eyes using the extraction of Facial Landmark points and analysis of a calculation result of the Eye Aspect Ratio (EAR). The System qualified works in real-time since it uses a particular library designed in a mobile application. Based on some experiments that have been done, the proposed method adequate to identify sleepy drivers accurately by 92.85%.

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“…The app is programmed within the Android Studio platform to use the geomagnetic rotation vector sensor of the smartphone to provide the angular orientation of the clubface relative to the line of the putt. A detailed description of programming using Android Studio has been provided by one of the authors (Hung) in a recent publication [15].…”

Section: Apparatusmentioning

confidence: 99%

Development of a Mobile Application for Testing the Effect of Vibration Feedback on Putting Performance

Semkewyc¹,

Suresh²,

Hung³

2018

Adv Comput Sci

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Many golfers have trouble with short putts. This could be either due to poor putting form, or they suffer psychologically from the yips. To reduce or potentially alleviate these issues, an application (app) was developed to provide vibration feedback for putter misalignment during the putting stroke. This was tested on three experimental subjects along with a control subject who did not receive vibration feedback. Five trials were conducted over a two-month period, where each trial consisted of 60 putt attempts. All experimental subjects showed an improvement in putting form, with both an increase in putts made, and a decrease in triggered vibration over the course of the trials. The average percentage of putts made for all three subjects improved from 25% to 50.6% over the five trials, while the average amount of vibration activation decreased from 35.1% to 32.2%. Conversely, the control subject showed a slight decline in performance through the five trials. The difference between the experimental group and the control was statistically significant (p<0.05). The results suggest that this app can be used by both novice and expert golfers to improve putting form by helping them maintain a straight putter swing path.

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Drowsy driver mobile application: Development of a novel scleral-area detection method

Cited by 16 publications

References 6 publications

Computer vision for total laparoscopic hysterectomy

Computer vision for total laparoscopic hysterectomy

Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values

Development of a Mobile Application for Testing the Effect of Vibration Feedback on Putting Performance

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