(Georgia Institute of Technology, 2020-12)
Patel, Devashru
Social interaction amongst students can often affect their university experience drastically. The manner in which students collaborate and socialize with their peers can also be used as a helpful metric to predict their academic performance. Traditional methods of quantifying social interaction like surveys suffer from reliability issues stemming from factors such as social desirability bias. In this research, our team investigates a method to predict academic performance that leverages Wi-Fi logs. The logs span a time period of 14 weeks and in their raw form can be used to estimate a student’s location, albeit with low spatial resolution. Analysis of these logs however can lead to fairly accurate inferences of collocations amongst students. We then found that a 0.75 rate of correlation exists between student performance predicted from these collocations and actual performance. These findings are significant in that they could demonstrate the utility of Wi-Fi data in applications such as well-being and mental health.
(Georgia Institute of Technology, 2020-05)
Xu, Ruihan
Virtual reality (VR) developed as an immersive communication tool, where users are able to interact with each other through virtual avatars. Among the current VR applications with human avatars, most of them emphasize tracking the upper face, like eye tracking. However, the lower face contains the largest range of motions on the face, yet is usually hard for commercial VR headset to capture. In this work, we implemented two VR communication systems that are focusing on lip and jaw motion: 1) facial landmark tracking, and 2) audio-driven lip synchronization. We then conducted users studies to compare these two systems and therefore explore the role of lip and jaw motions when VR is used as a communication medium.
(Georgia Institute of Technology, 2018-12)
Bercik, Bailey
As mobile devices and wearables become smaller and more portable, their development becomes limited in size and hardware capabilities. This leads to difficulties with input as the size of a user’s finger can narrow the possible interactions that can be performed or occlude the screen, limiting visual feedback from the device. Inspiring by SoundTrak which presents 3D acoustic sensing techniques for smartwatches, LeapTrak expands upon this previous research through presenting a set of gestures performed in 3D space for smartwatch applications using a Leap Motion [16].