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PRO-Fit: A personalized fitness assistant framework
Proceedings of the 28th Intl. Conf. on Software Engineering and Knowledge Engineering (2016)
  • Saumil Dharia, San Jose State University
  • Vijesh Jain, San Jose State University
  • Jvalant Patel, San Jose State University
  • Jainikkumar Vora, San Jose State University
  • Shaurya Chawla, San Jose State University
  • Magdalini Eirinaki, San Jose State University
The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous feedback from the end users and fail to engage those who have busy schedules, or are not as committed and self-motivated. In this work, we propose a framework that employs machine learning and recommendation algorithms in order to smartly track and identify user’s activity by collecting accelerometer data, synchronizes with the user’s calendar, and recommends personalized workout sessions based on the user’s and similar users’ past activities, their preferences, as well as their physical state and availability. 
  • wearable technology,
  • activity tracking,
  • classification,
  • recommendation,
  • personalized assistant
Publication Date
Publisher Statement
This is the Accepted Version of a work that appeared in Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering, 2016. The Version of Record is available at the following link:
Citation Information
Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, et al.. "PRO-Fit: A personalized fitness assistant framework" Proceedings of the 28th Intl. Conf. on Software Engineering and Knowledge Engineering (2016)
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