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02641nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8794 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251112100349.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251112b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T60 D37 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Daroya, Renz C.; Dela Cruz, Donnabel C. |
| 245 ## - TITLE STATEMENT |
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| Title |
Adaptation of fitness application through AI integration: An android based application for home workout fitness experience |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
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unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: The “iFit AI” fitness application is a user-friendly platform created to assist gym beginners, users who prefer to workout at home, gym enthusiasts, and gym instructors in determining the suitable workout for different body types. Many beginners struggle to because they lack guidance on workouts, diet, and exercise intensity. This study aims to bridge that gap by utilizing predictive analytics to offer personalized workout routines and diet recommendations. By analyzing a user’s past performance, physical attributes, and other relevant data, the app can forecast the outcomes of specific workouts, leading to tailored exercise and nutrition plans. These customized approaches enhance fitness efficiency, minimize injury risk, and promote overall health. Additionally, a progress tracker will motivate users by showing their achievements toward their fitness goals. The developers will use Multi-Linear Regression (MLR) to predict results based on users consistent efforts, providing actionable insights for workout and diet adjustments. Users will also have the option to upload photos for visual progress tracking using TensorFlow. To boost engagement, the app will feature a rewards system that incentivizes users upon reaching goals while recommending appropriate food options to align with their fitness objectives. The approach has been successful in correctly identifying different proper workouts and calorie intake for each body type. iFit AI has proved its ability to appropriately recommended and predict through thorough testing. This achievement demonstrates TensorFlow’s usefulness in this setting while also validating the system’s design and implementation. While effective, the researchers believe that utilizing algorithms created specifically for complicated visual data could result in even greater accuracy. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
Thesis/Dissertation |