University
Shawnee State University
Major
Computer Engineering Technology
Student Type
Undergraduate Student
Presentation Types
Poster Presentation (Live)
Keywords:
artificial intelligence, swimming, performance analysis, object detection
Abstract
This research project focuses on the initial stages of developing and implementing a smart robot assistant swimming coach. Our work focuses on successfully implementing a swimmer detection system and establishing the groundwork for swimmer tracking functionality. We have created a detection system to identify swimmers in various pool environments by integrating commercial minicomputers with artificial intelligent algorithms. Our preliminary work on the tracking component shows promising results for following swimmer movements throughout training sessions. The completed detection system addresses the first critical challenge in developing automated swimming feedback. While the comprehensive coaching functionality remains under development, this initial phase represents an essential foundation for future work. The swimmer detection system we have developed demonstrates the feasibility of the broader vision to provide swimmers with real-time feedback, setting the stage for continued innovation in sports technology for competitive swimming.
Human and Animal Subjects
no
IRB or IACUC Approval
no
Faculty Mentor Name
JT Ok
Faculty Mentor Title
Assistant Professor
Faculty Mentor Department
Engineering Technologies
Recommended Citation
Lauders, Parker; Salyers, Audrianna; Johnson, Avery; and Moore, Zoe, "Development and Implementation of Smart Robot Assistant Swimming Coach" (2025). Celebration of Scholarship. 3.
https://digitalcommons.shawnee.edu/cos/2025/posters/3
Location
Morris UC Lobby
Development and Implementation of Smart Robot Assistant Swimming Coach
Morris UC Lobby
This research project focuses on the initial stages of developing and implementing a smart robot assistant swimming coach. Our work focuses on successfully implementing a swimmer detection system and establishing the groundwork for swimmer tracking functionality. We have created a detection system to identify swimmers in various pool environments by integrating commercial minicomputers with artificial intelligent algorithms. Our preliminary work on the tracking component shows promising results for following swimmer movements throughout training sessions. The completed detection system addresses the first critical challenge in developing automated swimming feedback. While the comprehensive coaching functionality remains under development, this initial phase represents an essential foundation for future work. The swimmer detection system we have developed demonstrates the feasibility of the broader vision to provide swimmers with real-time feedback, setting the stage for continued innovation in sports technology for competitive swimming.
