Vol. 5 No. 1 (2026): January
RESEARCH ARTICLES

Fitaura: A Minimal-Interface Fitness Platform for Improving Consistency Through Personalized Guidance

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  • R Tamilselvi,
  • Varshaa R S,
  • Anannya M,
  • Angamuthu S,
  • Sabari M
R Tamilselvi
Department of Computer Science and Design, SNS College of Technology, Coimbatore, Tamil Nadu, India.
Varshaa R S
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Anannya M
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Angamuthu S
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Sabari M
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
DOI: https://doi.org/10.5281/zenodo.17975420

Published 2025-12-18

Keywords

  • Fitness App,
  • Habit Tracking,
  • Personalized Motivation,
  • Optional Nutritionist Chat,
  • Mobile Application,
  • AI-Suggested workout Plans
    • ...More
    Less

How to Cite

R Tamilselvi, Varshaa R S, Anannya M, Angamuthu S, & Sabari M. (2025). Fitaura: A Minimal-Interface Fitness Platform for Improving Consistency Through Personalized Guidance. International Journal of Computational Learning & Intelligence, 5(1), 946–954. https://doi.org/10.5281/zenodo.17975420

Copyright (c) 2025 R Tamilselvi, Varshaa R S, Anannya M, Angamuthu S, Sabari M

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Most fitness applications can track steps, calories, and heart rate, but users still fail to remain consistent with their routines. This happens because tracking alone does not create motivation or provide personal guidance. Fitaura is a personalized fitness application developed to solve this behavioural challenge. It focuses on habit formation through streak tracking, small daily goals, mood check-in, AI-based suggestions, and direct nutritionist support. Instead of overwhelming users with graphs and numerical data, the application provides simple visual progress and motivational reminders. A prototype of the app was created using Figma with a clean user interface and accessible controls. User testing showed that people found Fitaura easy to navigate and helpful for maintaining daily consistency. The results prove that personalized encouragement and expert guidance lead to better usability and long-term engagement.
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References

  1. Valivarthi, D. T., Kethu, S. S., Natarajan, D. R., Narla, S., Peddi, S., & Kurunthachalam, A. (2025). Enhanced Medical Anomaly Detection Using Particle Swarm Optimization-based Hybrid MLP-LSTM Model. International Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/s0218001425570228.
  2. Optimizing Task Offloading in Vehicular Network (OTO): A Game Theory Approach Integrating Hybrid Edge and Cloud Computing. (2025). Journal of Cybersecurity and Information Management, 15(1). https://doi.org/10.54216/jcim.150110.
  3. Vallu, V. R., Pulakhandam, W., Kurunthachalam, A., & Hugar, S. (2025). PR-MICA and SGELNN: A Unified Framework for Feature Extraction in Graph Learning. 2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC), 864–869. https://doi.org/10.1109/aic66080.2025.11211928.
  4. Rao, V. V., Jagathpally, A., Pulakhandam, W., Shahwar, T., & Kurunthachalam, A. (2025). A Vision Transformers Approach for Surgical Monitoring with Algorithmic Framework and Experimental Evaluation. 2025 International Conference on Biomedical Engineering and Sustainable Healthcare (ICBMESH), 1–6. https://doi.org/10.1109/icbmesh66209.2025.11182237.
  5. Jadon, R., Budda, R., Gollapalli, V. S. T., Chauhan, G. S., Srinivasan, K., & Kurunthachalam, A. (2025). Grasp Pose Detection and Feature Extraction Using FHK-GPD and Global Average Pooling in Robotic Pick-and-Place Systems. 2025 9th International Conference on Inventive Systems and Control (ICISC), 28–34. https://doi.org/10.1109/icisc65841.2025.11188246.
  6. Vallu, V. R., Pulakhandam, W., & Kurunthachalam, A. (2025). Revolutionizing Mobile Cloud Security: Employing Secure Multi-Party Computation and Blockchain Innovations for E-Commerce Platforms. 2025 International Conference on Artificial Intelligence and Emerging Technologies (ICAIET), 1–6. https://doi.org/10.1109/icaiet65052.2025.11211015.
  7. Vallu, V. R., Pulakhandam, W., Jagathpally, A., Shahwar, T., & Kurunthachalam, A. (2025). Object Recognition and Collision Avoidance in Robotic Systems Using YOLO and HS-CLAHE Techniques. 2025 5th International Conference on Intelligent Technologies (CONIT), 1–6. https://doi.org/10.1109/conit65521.2025.11166833.
  8. Jadon, R., Budda, R., Gollapalli, V. S. T., Singh Chauhan, G., Srinivasan, K., & Kurunthachalam, A. (2025). Innovative Cloud-Based E-Commerce Fraud Prevention Using GAN-FS, Fuzzy-Rough Clustering, Smart Contracts, and Game-Theoretic Models. 2025 International Conference on Computing Technologies &Amp; Data Communication (ICCTDC), 1–6. https://doi.org/10.1109/icctdc64446.2025.11158048.
  9. Gayathri, R., Sheela Sobana Rani, K., & Aravindhan, K. (2024). Classification of Speech Signal Using CNN-LSTM. Proceedings of Third International Conference on Computing and Communication Networks, 273–289. https://doi.org/10.1007/978-981-97-2671-4_21.
  10. Asharaf, T., Mathew, A. K., Simman, R., Santhosh, S., Sruthi, S., & Dharshini, Y. S. (2025). Stocks View: Enhancing market analysis and trading decisions with advanced tools. In Proceedings of the 4th International Conference on Emerging Technologies in Computer Science and Engineering. European Alliance for Innovation (EAI). https://www.semanticscholar.org/author/Thasni-Asharaf/2387618460
  11. Rahman, A., & Abdullah, S. (2022). Adoption of digital fitness applications in developing countries. International Journal of Digital Health.
  12. Thompson, J., & Brown, A. (2021). Motivational factors influencing long-term fitness app engagement. Journal of Behavioral Health.
  13. Ahmed, S. T., Kumar, V. V., & Jeong, J. (2024). Heterogeneous workload-based consumer resource recommendation model for smart cities: EHealth edge–cloud connectivity using federated split learning. IEEE Transactions on Consumer Electronics, 70(1), 4187-4196.
  14. Kumar, S. S., Ahmed, S. T., Sandeep, S., Madheswaran, M., & Basha, S. M. (2022). Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques. Computers, Materials & Continua, 72(1).
  15. Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755.
  16. Fathima, A. S., Basha, S. M., Ahmed, S. T., Mathivanan, S. K., Rajendran, S., Mallik, S., & Zhao, Z. (2023). Federated learning based futuristic biomedical big-data analysis and standardization. Plos one, 18(10), e0291631.
  17. Siddiqha, S. A., & Islabudeen, M. (2023, January). Web-Page Content Classification on Entropy Classifiers using Machine Learning. In 2023 International Conference for Advancement in Technology (ICONAT) (pp. 1-5). IEEE.