SRL MS Thesis Defense of Jung In Koh. Thursday, June 15. Title: Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication

Thesis Defense
Thursday, June 15

Title: Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emoji in Computer-Mediated Communication

Jung In Koh

1 PM Thursday, June 15, 2017

Teague 326

Abstract

Recent trends in computer-mediated communications (CMC) have not only led to expanded instant messaging (IM) through the use of images and videos, but have also expanded traditional text messaging with richer content, so-called visual communication markers (VCM) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Additionally, conventional ways for accessing VCMs continues to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. One such form of expressive nonverbal that does exist and is well-studied come in the form of hand gestures.
In this work, I propose a user-defined hand gesture set that is highly representative to VCMs and a two-stage hand gesture recognition system (feature-based, shape based) that distinguishes the user-defined hand gestures. The goal of this research is to provide users to be more immersed, natural, and quick in generating VCMs through gestures. The idea is for users to maintain the lower-bandwidth online communication of text messaging to largely retain its convenient and discreet properties, while also incorporating the advantages of higher-bandwidth online communication of video messaging by having users naturally gesture their emotions that are then closely mapped to VCMs. Results show that the accuracy of user-dependent is approximately 86% and the accuracy of user independent is about 82%. 

Biography

Jung In Koh is a Master's student in the Department of Computer Science and Engineering at Texas A&M University and a research assistant in the Sketch Recognition Lab. Before joining Texas A&M, she received the bachelor's degree in Computer Science from Sookmyung Women's University in South Korea. Her research interests include motion-detection and data mining.

Advisor: Dr. Tracy Hammond

 

SRL MS Thesis Defense of Seth Polsley. Monday, June 5. Title: Identifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communication

Thesis Defense
Monday, June 5

Title: 

Identifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communication

Seth Polsley

3 PM Monday, June 5, 2017

Teague 326

Abstract

Between appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; etc. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the utlimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making.

Biography

Seth Polsley is a graduate student in the Department of Computer Science and Engineering at Texas A&M University and a research assistant in both the Sketch Recognition Lab and College of Medicine Biomedical Informatics Research group. Before joining A&M, he received a B.S. in Computer Engineering from the University of Kansas where he worked with the Speech and Applied Neuroscience Lab. His research interests may be broadly described as intelligent systems, which has led to work on multiple learning- based systems in the domains of education and health.

 

Advisor: Dr. Tracy Hammond

SRL MS Thesis Defense of Josh Cherian. Friday, December 9. Title: Recognition of Everyday Activities through Wearable Sensors and Machine Learning

Thesis Defense
Friday, December 9

Title: Recognition of Everyday Activities through Wearable Sensors and Machine Learning

Josh Cherian

10 AM Friday, December 9, 2016

Teague 326

Abstract
Over the past several years, the use of wearable devices has increased dramatically, largely due to their increasingly smaller and more personal form factors, greater sensor reliability, and increasing utility and affordability.  This has helped many people live healthier lives and achieve their personal fitness goals, as they are able to quantifiably and graphically see the results of their efforts every step of the way. While these systems work well within the fitness domain, they have yet to achieve a convincing level of functionality in the larger domain of healthcare.To facilitate the increased use of wearable devices to aid in healthcare, we present a two tier recognition system for identifying health activities in real time based on accelerometer data. To do this we run a series of users studies to collect data for six everyday activities: brushing one's teeth, combing one's hair, scratching one's chin, washing one's hands, taking medication, and drinking, achieving an f-measure of 0.85 when identifying these activities in a controlled setting. To evaluate our recognition system's ability to recognize activities in a naturalistic setting, we identify instances of brushing teeth over the course of a day. We initially achieve an f-measure of 0.68; however we are able to improve this to 0.85 by proposing and extracting several novel features. Through recognition of these activities, we aim to encourage the use of wearable devices for everyday personal health management. 

Biography
Josh Cherian is a MS candidate at Texas A&M University in the department of Electrical Engineering working under Dr. Tracy Hammond in the Sketch Recognition Lab. Josh completed his BS in Biomedical Engineering from Georgia Institute of Technology. His primary research interest is activity recognition, with a specific focus on recognizing daily health activities such as brushing one's teeth, washing one's hands, and taking medication.

Advisor: Dr. Tracy Hammond

    

SRL MS Thesis Defense of Nahum Villanueva Luna. Monday, March 6. Title: ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience

Thesis Defense
Monday, March 6

Title: ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience

Nahum Villanueva Luna

10 AM Monday, March 6, 2017

516 H.R. Bright Building

Abstract

ARCaching is an augmented reality application designed to help Geocachers on their quest to find hidden containers around the world. Geocaching is a popular treasure hunting game that uses GPS

coordinates and mobile devices to guide players to hidden object. ARCaching is trying to test the effects that using augmented reality on this kind of tasks could have and if it helps to improve the user's experience while Geocaching. 

Biography

  Born on Mérida Yucatán Mexico on April 29 in 1991. I got my bachelor's degree as a Software Engineer on 2013 at "Universidad Autónoma De Yucatán" in Mexico. I'm currently finishing my master's degree on computer engineering at Texas A&M at the Sketch Recognition Lab.

Advisor: Dr. Tracy Hammond

 

SRL MS Thesis Defense of Jorge I. Herrera-Camara. Friday, March 3. Title: Flow2code - From Hand-drawn Flowchart to Code Execution

Thesis Defense

Friday, March 3

Title:   Flow2code - From Hand-drawn Flowchart to Code Execution

Jorge I. Herrera-Camara

10:00 AM, March 3, 2017

Location: 516 H.R. Bright Building

Abstract

Flowcharts play an important role when learning programming by conveying algorithms graphically and making them easy to read and understand. When learning how to code with flowcharts and the transition between the two, people often use computer based software to design and execute the algorithm conveyed by the flowchart. This require the users to learn how to use the computer based software which often leads to a steep learning curve. Using off- line sketch recognition and computer vision algorithms on a mobile device the learning curve can decrement, by drawing the flowchart on a piece of paper and using a mobile device with a camera to be able to capture it. Flow2Code is a code flowchart recognizer that allows the users to code simple scripts on a piece of paper by drawing flowcharts. This approach attempts to be more intuitive since the user does not need to learn how to use a system to design the flowchart. Only a pencil, a notebook with white pages and a mobile device is needed to achieve the same result. The main contribution of this thesis would be to provide a more intuitive and easy to use tool for people to translate and execute flowcharts into code. 

 Biography

Born in Yucatan, Mexico, studied software engineering as undergraduate back home. I have worked in Software Engineering roles for two years after graduating from undergrad. Then decided to start my MS in Computer Science here at A&M in 2015.

Advisor: Dr. Tracy Hammond 

 

 

SRL MS Thesis Defense of Aqib Bhat. Thursday, March 2. Title: Sketchography - Automatic Grading of Map Sketches for Geography Education

Thesis Defense

Thursday, March 2

 

Title: Sketchography - Automatic Grading of Map Sketches for Geography Education

Aqib Bhat

10 am, Thursday, March 2, 2017

Room 326 Teague Building

Abstract

Geography is a vital classroom subject that teaches students about the physical features of the planet we live on. Despite the importance of geographic knowledge, almost 75% of 8th graders scored below proficient in geography on the 2014 National Assessment of Educational Progress. Sketchography is a pen- based intelligent tutoring system that provides real-time feedback to students learning the locations, directions, and topography of rivers around the world. Sketchography uses sketch recognition and artificial intelligence to understand the user’s sketched intentions. As sketches are inherently messy, and even the most expert geographer will draw only a close approximation of the river’s flow, data has been gathered from both novice and expert sketchers. This data, in combination with professors’ grading rubrics and statistically driving AI-algorithms, provide real-time automatic grading that is similar to a human grader’s score. Results show the system to be 94.64% accurate compared to human grading. 

Biography

Aqib Niaz Bhat obtained his Bachelor of Technology degree in Electronics and Communication engineering from National Institute of Technology, Srinagar, India. He is currently a Computer Science Master's thesis student in the department of Computer Science & Engineering at Texas A&M University, College Station. He has worked as a software engineer with Wipro Technologies and interned with Amazon.com. Aqib is a member of the Sketch Recognition Lab.

Advisor: Dr. Tracy Hammond