ORCID
Rachel Guynes: https://orcid.org/0009-0007-7230-4694
Degree
Bachelor of Science (B.S.) in Computer Science
Major(s)
Computer Science
Document Type
Immediate Open Access
Abstract
One of the many fields that has seen the integration of robots is therapy. Zoomorphic robots (ZR) are designed to look and behave like animals to assist in Animal Assisted Therapy (AAT) practices. Studies show that ZRs can provide benefits similar to working with an actual animal; however, their high cost limits their accessibility. This thesis documents the process of building a real-time, low-cost motion classification system that can be attached to a stuffed animal to make it more interactive. Using a Random Forest (RF) classifier, the system identifies movements with approximately 81.67% accuracy.
Date Defended
4-30-2026
Thesis Director
Jingdao Chen
Second Committee Member
Cindy Bethel
Third Committee Member
Matthew Peaple
Recommended Citation
Guynes, Rachel N., "A Low-Cost Motion Classification System for a Stuffed Animal Using an IMU and Machine Learning" (2026). Honors Theses. 193.
https://scholarsjunction.msstate.edu/honorstheses/193
Included in
Other Electrical and Computer Engineering Commons, Robotics Commons, VLSI and Circuits, Embedded and Hardware Systems Commons
