International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Predicting and Analyzing Human Daily Routine Using Machine Learning

Abstract

Increasing productivity hinges on motivation, yet often individuals inadvertently lose productivity due to health issues and inefficient time management. In the tech domain, various wearable devices like watches, belts, and cameras have emerged to monitor and offer productivity recommendations. However, contemporary society calls for a more intelligent solution - a single mobile application capable of behavior monitoring without external devices. This research delves into such a solution, aiming to comprehend and predict daily human routines via a mobile app, eliminating the need for wearables. The central focus encompasses the detection of sleep patterns, location tracking, food consumption monitoring, and emotion tracking. The ultimate goal is to understand and forecast these facets of user behavior and evaluate their impact on productivity. Leveraging mobile phone sensors for data collection obviates the need for additional hardware. The accumulated data feeds into machine learning models to predict routines. The study's outcomes aspire to provide insights into individual daily behaviors and empower the application to encourage users to make adjustments that bolster productivity. This research contributes to the field by harnessing smartphone technology to enhance users' understanding of their behaviors and optimize their daily routines.

Country : Sri Lanka

1 P.A.L.N. Weerasinghe2 M.A.P.H.N. Mudannayake3 S.D. Wijesinghe4 N.D. Malwenna Hewage

  1. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 437-444

doi.org/10.47001/IRJIET/2023.710058

Full Paper
Download

References

  1. Cheng, P., Zhang, Y., Wang, T., Wang, H., Yang, F., & Zhou, J. (2019). Sleep stage classification is based on unsupervised feature learning and clustering. IEEE.
  2. Transactions on Neural Systems and Rehabilitation Engineering, 27(10), 2084-2094. doi: 10.1109/TNSRE.2019.2930362.
  3. Kang, H., Kim, J. W., & Lee, J. H. (2019). Self-organizing map-based classification of sleep stages using cardiorespiratory signals from a wearable device. IEEE Transactions on Biomedical Engineering, 66(8), 2353-2361. doi: 10.1109/TBME.2019.2899649.
  4. Wang, R., Chen, F., Chen, Z., Li, T., & Harari, G. (2016). Tracking User Sleep Quality via Smartphones: Possibilities and Challenges. ACM Transactions on Computer-Human Interaction, 23(4), 1-27. https://doi.org/10.1145/2988243.
  5. J. A. Smith, E. R. Johnson, and D. M. Brown, "Shared Location Prediction Using a Common Dataset," in Proceedings of the International Conference on Machine Learning (ICML), 2018.
  6. S. Chen, X. Wang, and Y. Zhang, "Unified Location Prediction Model for Latitude and Longitude Estimation," in Proceedings of the International Conference on Machine Learning (ICML), 2019.
  7. Dong, H., Ding, X., & Qian, W. (2020). Prediction of next location based on user's historical data using machine learning algorithm. In 2020 IEEE 3rd International Conference on Electronics Technology (ICET) (pp. 1567-1571). IEEE.
  8. Nur Alia Syahirah Badruhisham, Nur Nabilah Abu Mangshor “Emotion Recognition Using Convolutional Neural Network”.
  9. H.M Shahzad, Sohail Masood Bhatti, Awais, Mahmood, Arfan Jaffar, “Hybrid Facial Emotion Recognition Using CNN-Based Features”, Faculty of Computer Science and Information Technology.
  10. Malina Jiang: “Food Image Classification with Convolutional Neural Networks”, 2019.
  11. Nareen O. M.Salim, Subhi R. M. Zeebaree, Mohammed A. M.Sadeeq: “Study for Food Recognition System Using Deep Learning”, 2021.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies