Full metadata
Title
Basketball Shooting Prediction Using Machine Learning Models and Motion Capture System
Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
Date Created
2023
Contributors
- Liang, Chengxu (Author)
- Ingalls, Todd (Thesis advisor)
- Turaga, Pavan (Thesis advisor)
- De Luca, Gennaro (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
38 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187831
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Computer Science
System Created
- 2023-06-07 12:39:19
System Modified
- 2023-06-07 12:39:24
- 1 year 5 months ago
Additional Formats