Proceedings of the 2016 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
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 commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in order to smartly track and identify user's activity, synchronizes with the user's calendar, recommends personalized workout sessions based on the user's preferences, fitness goals, and availability. Moreover, PRO-Fit integrates with the user's social network and recommends “fitness buddies” with similar preferences and availability.
Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, and Iraklis Varlamis. "PRO-Fit: Exercise with friends" Proceedings of the 2016 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016). https://doi.org/10.1109/ASONAM.2016.7752437
This is the Accepted Version of an article published in the Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). The Version of Record is available online at this link.
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