Problem Statement
This project investigates how to design a motion tracking system that can be used to support learning in the elementary school classroom. Key challenges include the simultaneous movement capture of both children and objects, a non-intrusive system suitable for the classroom environment, and cost-effectiveness.
We are also investigating puppet-based tracking systems, as a different approach where the children enact through a puppet. The goal is to identify which performs better to support storytelling: the puppet system or full-body enactment.
Proposed Approach
- Use of colored or infrared LEDs as markers
- Fusion of two or more Kinects at different angles
- Integration of Inertial Measurement Units (IMUs) with markers to improve the vision tracking
- Comparison of portable systems with higher-end less portable systems (e.g., OptiTrack) for classroom use
- Using deep learning for tracking and categorizing objects for marker-less tracking
- Capturing emotion by facial expression and speech, for more expressive enactment animation
Project Team Members
- Dr. Sharon Lynn Chu (ELX Lab Director)
- Nanjie (Jimmy) Rao (Ph.D. Student, Computer Information Science & Engineering)
- Ranger Chenore (Undergraduate Student, University of Florida)
- Grace Nemanic (Undergraduate Student, University of Florida)
- Lara Disuanco (Undergraduate Student, University of Florida)
- Yvette Williamson (Undergraduate Student, University of Florida)
COLLABORATORS
- Dr. Francis Quek (College of Architecture, Texas A&M University)
- Niloofar Zarei (Ph.D. Student, Texas A&M University)