Ahli: Workout management mobile app
The AHLI: Workout Management Mobile App is intended to give customers with individualized workout regimens, progress monitoring, and gym finding via the use of AI and cloud services. The app uses Google Gemini AI to create personalized workout regimens and the Google Places API to assist users fi...
| Main Author: | |
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6876/ http://eprints.utar.edu.my/6876/1/fyp_DE_2024_TAYJ.pdf |
| _version_ | 1848886788313055232 |
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| author | Tam, Alvin Yun Jie |
| author_facet | Tam, Alvin Yun Jie |
| author_sort | Tam, Alvin Yun Jie |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | The AHLI: Workout Management Mobile App is intended to give customers with
individualized workout regimens, progress monitoring, and gym finding via the use of AI and
cloud services. The app uses Google Gemini AI to create personalized workout regimens and
the Google Places API to assist users find local gyms, while Firebase is utilized for data
management and real-time synchronization. During development, the app encountered
problems such as API rate constraints and compatibility concerns with Expo and React Native,
which were resolved to provide a seamless user experience. Users expressed high levels of
satisfaction with the app's use, design, and essential functions. Despite these obstacles, the
software efficiently assists users in managing their fitness goals with easy interfaces and handy
monitoring functions. Future enhancements might include adding social interaction features
and enhancing API speed to improve the overall user experience. |
| first_indexed | 2025-11-15T19:44:03Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6876 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:03Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-68762025-02-14T06:56:29Z Ahli: Workout management mobile app Tam, Alvin Yun Jie H Social Sciences (General) LB Theory and practice of education T Technology (General) The AHLI: Workout Management Mobile App is intended to give customers with individualized workout regimens, progress monitoring, and gym finding via the use of AI and cloud services. The app uses Google Gemini AI to create personalized workout regimens and the Google Places API to assist users find local gyms, while Firebase is utilized for data management and real-time synchronization. During development, the app encountered problems such as API rate constraints and compatibility concerns with Expo and React Native, which were resolved to provide a seamless user experience. Users expressed high levels of satisfaction with the app's use, design, and essential functions. Despite these obstacles, the software efficiently assists users in managing their fitness goals with easy interfaces and handy monitoring functions. Future enhancements might include adding social interaction features and enhancing API speed to improve the overall user experience. 2024-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6876/1/fyp_DE_2024_TAYJ.pdf Tam, Alvin Yun Jie (2024) Ahli: Workout management mobile app. Final Year Project, UTAR. http://eprints.utar.edu.my/6876/ |
| spellingShingle | H Social Sciences (General) LB Theory and practice of education T Technology (General) Tam, Alvin Yun Jie Ahli: Workout management mobile app |
| title | Ahli: Workout management mobile app |
| title_full | Ahli: Workout management mobile app |
| title_fullStr | Ahli: Workout management mobile app |
| title_full_unstemmed | Ahli: Workout management mobile app |
| title_short | Ahli: Workout management mobile app |
| title_sort | ahli: workout management mobile app |
| topic | H Social Sciences (General) LB Theory and practice of education T Technology (General) |
| url | http://eprints.utar.edu.my/6876/ http://eprints.utar.edu.my/6876/1/fyp_DE_2024_TAYJ.pdf |