#Android eye tracking tv#

The model takes as input an RGB image from a smartphoneРІР‚в„ўs front-facing camera cropped to the eye regions, and applies three layers of convolution to extract gaze features. We trained a multi-layer feed-forward convolutional neural network (ConvNet). However, their accuracy has been too low for rigorous eye movement research (2.56РІР‚вЂњ3 РІв‚¬В for laptops 39, 40 and 2.44РІР‚вЂњ3 РІв‚¬В viewing angle for smartphones 37, 38 compared to 0.5РІР‚вЂњ1 РІв‚¬В for specialized eye trackers). Recent approaches in machine learning (ML) have shown promise for eye tracking using the existing front-facing cameras (selfie cameras) on smartphones 37, 38 and laptops 39, 40.

Given their pervasiveness, accurate and affordable eye tracking on smartphones could enable significant advances in eye movement research by providing orders-of-magnitude scaling and generating insights across diverse populations, as well as unlocking applications across vision research, accessibility and healthcare.

#Android eye tracking tv#

Recent estimates show over 2.8 billion smartphone users worldwide 35, with nearly twice as much time spent consuming content on mobile devices as desktop/laptop in the US (increases to 3Р“вЂ” in India, 6Р“вЂ” in China), and exceeding time spent watching TV 36. Further, little is known about eye movement behavior on small smartphone displays as most prior research focused on large desktop displays. There are some cheaper eye tracking solutions available for the desktop 33, 34, though not for mobile screens (state-of-the-art mobile eye trackers cost on the order of ten thousand USD). The underlying methodology, known as eye tracking, has been used for decades as a reliable way to measure eye movements 30, 31, 32.Р”Вespite the numerous benefits of eye tracking, research and applications have been limited by the high cost of eye trackers and their inability to scale due to the use of specialized hardware (e.g., infrared light source, multiple high spatio-temporal resolution infrared cameras). Understanding eye movements has been central to research in attention and visual processing in the brain, including focus areas such as visual search 11, 12, 13, scene perception 14, 15, 16, and reading 17, 18, to name a few.Р”В«eyond basic vision research, eye movements have also been of interest to the broader research community with applications ranging from saliency models for visual content analysis 19, design evaluation 20, usability and consumer behavior research 21, 22, 23, driving 24, gaming 25, 26, gaze-based interaction for accessibility 27 to medical research 28, 29. Thus, eye movements offer a direct way to measure overt spatial attention, and have been considered by some to provide a window into the brain and mind 9, 10.

The human eye moves 3РІР‚вЂњ4 times per second on average, pausing to sample information from those important scene regions 6, 7, 8. Selective attention is the mechanism by which our brain selects and focuses on a few important scene regions for cognitive and visual processing (see refs. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.Р”Р„s we move through rich and complex environments in our everyday life, the retina is bombarded with vast amounts of visual information of ~10 10РІР‚вЂ°bits/s 1, 2. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Eye tracking has been widely used for decades in vision research, language and usability.