A Flutter-Based Mobile System for Real-Time Fitness and Activity Tracking

Mobile health (mHealth) has moved from novelty to necessity, helping people make sense of daily activity and long- term habits. This paper presents a cross-platform fitness tracker built with Flutter and Firebase that feels light, respects battery life, and treats privacy as a first-class feature. We integrate accelerometer, gyroscope, and GPS for step, distance, and route estimation; pair that with Firebase Authentication and Firestore for secure sync; and layer on top a simple, motivating UI that surfaces trends rather than raw numbers. Beyond implementation, we ground design choices in contemporary evidence: (i) what makes users continue to use fitness apps, (ii) how accurate phone- based step counts can be, and (iii) what drains energy on phones and how to avoid it. Bench tests with controlled walking tasks demonstrate stable step detection and distance estimation with low variance across speeds, while energy profiling indicates minimal overhead under Android power restrictions. We conclude with a privacy checklist, threat model, and deployment guidance to scale responsibly. In addition, the work highlights usability lessons gathered during iterative prototyping, showing how small adjustments in layout, color contrast, and feedback frequency noticeably improve user comfort. The system’s modular structure also makes it adaptable for future health features such as heart-rate analytics or sleep–wake pattern insights. Finally, the paper underscores the importance of transparent data handling, ensuring that users feel in control of what they share and how long it is stored.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-13
DOI
https://doi.org/10.5281/zenodo.18229042
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
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article

A Flutter-Based Mobile System for Real-Time Fitness and Activity Tracking

Gajanan M. Naik, B. J. Mithil Reddy
Zenodo (CERN European Organization for Nuclear Research)
Context-Aware Activity Recognition Systems
article

A Flutter-Based Mobile System for Real-Time Fitness and Activity Tracking

Gajanan M. Naik, B. J. Mithil Reddy
article en

Abstract

Mobile health (mHealth) has moved from novelty to necessity, helping people make sense of daily activity and long- term habits. This paper presents a cross-platform fitness tracker built with Flutter and Firebase that feels light, respects battery life, and treats privacy as a first-class feature. We integrate accelerometer, gyroscope, and GPS for step, distance, and route estimation; pair that with Firebase Authentication and Firestore for secure sync; and layer on top a simple, motivating UI that surfaces trends rather than raw numbers. Beyond implementation, we ground design choices in contemporary evidence: (i) what makes users continue to use fitness apps, (ii) how accurate phone- based step counts can be, and (iii) what drains energy on phones and how to avoid it. Bench tests with controlled walking tasks demonstrate stable step detection and distance estimation with low variance across speeds, while energy profiling indicates minimal overhead under Android power restrictions. We conclude with a privacy checklist, threat model, and deployment guidance to scale responsibly. In addition, the work highlights usability lessons gathered during iterative prototyping, showing how small adjustments in layout, color contrast, and feedback frequency noticeably improve user comfort. The system’s modular structure also makes it adaptable for future health features such as heart-rate analytics or sleep–wake pattern insights. Finally, the paper underscores the importance of transparent data handling, ensuring that users feel in control of what they share and how long it is stored.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 90%
Context-Aware Activity Recognition Systems
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