A study on tiredness assessment by using eye blink detection

In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy

My Lovely Granny’s Farm : An immersive virtual reality training system for children with autism spectrum disorder

One of the biggest difficulties faced by children with Autism Spectrum Disorder during their learning process and general life, is communication and social interaction. In recent years, researchers and practitioners have invested in different approaches to improving aspects of their communication and learning. However, there is still no consolidated approach and the community is still looking for new approaches that can meet this need. Addressing this challenge, in this article we propose a novelty approach (i.e., an Adaptive Immersive Virtual Reality Training System), aiming to enrich social interaction and communication skills for children with Autism Spectrum Disorder. In this adaptive system (called My Lovely Granny’s Farm), the behavior of the virtual trainer changes depending on the mood and actions of the users (i.e., patients/learners). Additionally, we conducted an initial observational study by monitoring the behavior of children with autism in a virtual environment. In the initial study, the system was offered to users with a high degree of interactivity so that they might practice various social situations in a safe and controlled environment. The results demonstrate that the use of the system can allow patients who needed treatment to receive therapy without leaving home. Our approach is the first experience of treating children with autism in Kazakhstan and can contribute to improving the communication and social interaction of children with Autism Spectrum Disorder. We contribute to the community of educational technologies and mental health by providing a system that can improve communication among children with autism and providing insights on how to design this kind of system.Peer reviewe

A study of left ventricular (LV) segmentation on cardiac cine-MR images

Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed dataset. For evaluation, we explored a benchmark dataset that was used for the MICCAI 3D segmentation challenge. We found that the cardiac MRI global thresholding has proved to be much efficient and robust than the adaptive thresholding. We achieved more than 92% accuracy for global thresholding, whereas, about 78% accuracy for the adaptive thresholding approach. The use of entropy or histogram to characterize segmentation in place of the intensity value of the pixel has a vital effect on segmentation efficiency. It is evident that the intensity information is corrupted by acquisition procedure, as well as the structure of organs. Due to the lack of boundary information in cardiac cine-MRI, clustering and region-based segmentation have produced more than 93% segmentation accuracy. For the case of soft clustering, the increased accuracy is found as 96%. However, more explorations are required, specially based on deep learning approaches on very large datasets