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2025 | OriginalPaper | Chapter

Deciphering Fitness Application Data Using Machine Learning

Authors : Sagar Puniyani, Dhruv Girotra, Divya Agarwal, Deepali Virmani

Published in: Innovative Computing and Communications

Publisher: Springer Nature Singapore

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Abstract

This paper predicts fitness application data of people using two machine learning techniques, linear regression and decision trees. Fitness Tracker collects data pertaining of physical activities such as steps, distance, calories burnt, sleep routine, etc. This paper explores the correlation between the aforementioned physical activities to find out which of the following affects calories burnt the highest. Comparison is done among two popular machine learning algorithms to depict their performance, interpretability, scalability, and applicability to the different datasets. This allows for us to maximize efficiency by reducing the collection of unnecessary data and further discuss suitable machine learning algorithms to implement in fitness devices for better accuracy in readings from fitness applications.

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Metadata
Title
Deciphering Fitness Application Data Using Machine Learning
Authors
Sagar Puniyani
Dhruv Girotra
Divya Agarwal
Deepali Virmani
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Innovative Computing and Communications

Print ISBN: 978-981-9741-51-9

Electronic ISBN: 978-981-9741-52-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-97-4152-6_37