Python-based approach for data analysis of physical fitness training among college students

Guan Li; Xinxin Huang

doi:10.1117/12.2686520

10 August 2023 Python-based approach for data analysis of physical fitness training among college students

Guan Li, Xinxin Huang

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127591A (2023) https://doi.org/10.1117/12.2686520
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

This thesis introduces a Python-based data processing system that utilizes wearable fitness devices to collect and analyze data on college students’ physical fitness training, progress, and overall health. The system stores the data in a MySQL database and analyzes it using statistical and machine-learning techniques. Its goal is to enhance the effectiveness of physical fitness programs in colleges and universities by providing trainers, coaches, and administrators with informed decision-making tools. The system’s performance will be evaluated using metrics such as accuracy, speed, and usability, with the ultimate aim of promoting a healthier lifestyle among college students.

Citation Download Citation

Guan Li and Xinxin Huang "Python-based approach for data analysis of physical fitness training among college students", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127591A (10 August 2023); https://doi.org/10.1117/12.2686520

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available