ABOUT ME

• 👋 Hi, I’m @jlucas1980

• 👀 I’m interested in biking, road trips and traveling to different countries.

• 🌱 I’m currently learning about ELAN, and working on completing my MSc in data science. I will be graduating Summer 2023.

• 💞️ I’m currently collaborating on The DREAM project; manipulating an avatar in the American Sign Language database using ELAN and Tableau. Main focus is using data gathered from ELAN to explain the connection between mouthing and just simple hand movements when commmunicating. Mouthing can be defined as the production of visual syllables with the mouth while signing. According to the article https://www.lifeprint.com/asl101/pages-layout/mouthinginasl2.htm "The use of American Sign Language requires more than just simple hand movements to convey a message. Without the use of proper facial expressions and other non manual signals (NMS), a message could be greatly misunderstood. Involved in these non manual signals are one's eyes, eyebrows, cheeks and mouth. These must be used to communicate clearly and effectively in daily conversations (Valli & Lucas, 21)." With the help of my team Meaghan Lidd and Nicole Barnekow we will gather data to look at specific mouth movements and the resemblance or correlation to spoken English. After creating the dataset from ELAN for the statistics, there was insufficient data. We decided on grouping the annotations for individual signs into categories. This annotation system consists of five classes, which are Fingerspelling, Lexical Variations, Indices, Classifier Predicates and Gestures. We will then use these classes to find commonalities between signers, using the mean, median and standard deviation of the spread within the classes, the latency/duration between signs and consistency between signers. After extensive research we found out that using the Wilcoxon non-parametric test to test the median values from the data overall was more effective to make comparisons across all participants. This test was applicable because the data used was in video format, meaning it was played to the viewer as thirty frames per second making our data nonparametric.

• 📫 How to reach me at dlucas14@depaul.edu

MY PROJECTS

-DeanniaVideo.eaf. and DeanniaVideo.pfsx were created with ELAN software for annotating.

-ELANVisualizationsinTableau.pptx shows different visualizations in Tableau using data from the "biker_biker_buddy" file in
elanBUcorpus.

-ELANpresentation.pptx shows examples of how to create an annotation and a tier in ELAN

MY ADVISOR*

Dr. Rosalee J. Wolfe Professor School of Computing Depaul University

The Division Director of Human-Computer Interaction and Computer Graphics, Dr. Wolfe was instrumental in establishing DePaul’s degree programs in Human-Computer Interaction, and Computer Graphics and Animation. After earning a Ph.D. in Computer Science from Indiana University, she was a NASA fellow at the Johnson Space Center, served on various committees of ACM SIGCSE and SIGGRAPH, held fellowships at Sony Imageworks, University of Hamburg, is a Fulbright Scholar and the team lead for the American Sign Language Project. She has been an excellent advisor/mentor during this research. Easy to connect with and breaks down each area so that I can easily understand the next steps in the research. Research Area: Graphics / Animation, Human Computer Interaction Specific Research Area: Character animation to support communication between Deaf and hearing communities; accessibility; Graphics pedagogy Email: wolfe@cs.depaul.edu Homepage: http://asl.cs.depaul.edu/

ABOUT MY RESEARCH***

I’m currently collaborating on The DREAM project; manipulating an avatar in the American Sign Language database using ELAN and Tableau. Main focus is using data gathered from ELAN to explain the connection between mouthing and just simple hand movements when commmunicating. Mouthing can be defined as the production of visual syllables with the mouth while signing. According to the article https://www.lifeprint.com/asl101/pages-layout/mouthinginasl2.htm "The use of American Sign Language requires more than just simple hand movements to convey a message. Without the use of proper facial expressions and other non-manual signals (NMS), a message could be greatly misunderstood. Involved in these non-manual signals are one's eyes, eyebrows, cheeks and mouth. These must be used to communicate clearly and effectively in daily conversations (Valli & Lucas, 21)." With the help of my team Meaghan Lidd and Nicole Barnekow we will gather data to look at specific mouth movements and the resemblance or correlation to spoken English. After creating the dataset from ELAN for the statistics, there was insufficient data. We decided on grouping the annotations for individual signs into categories. This annotation system consists of five classes, which are Fingerspelling, Lexical Variations, Indices, Classifier Predicates and Gestures. We will then use these classes to find commonalities between signers, using the mean, median and standard deviation of the spread within the classes, the latency/duration between signs and consistency between signers. After extensive research we found out that using the Wilcoxon non-parametric test to test the median values from the data overall was more effective to make comparisons across all participants. This test was applicable because the data used was in video format, meaning it was played to the viewer as thirty frames per second making our data nonparametric.