Social Recommendations for Personalized Fitness Assistance

Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively “high maintenance” of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work, we present the PRO-Fit framework, a personalized fitness assistant aiming at engaging users in fitness activities, incorporating a social element. The PRO-Fit architecture collects information from activity tracking devices and automatically classifies their activity type. Moreover, the framework incorporates a social recommender system. Using collaborative filtering on user profile and activity data, PRO-Fit generates personalized fitness schedules based on their availability and wellbeing goals. We also incorporate the social network community of the application’s users and identify different tie strengths based on the user’s connections and location. The output of the recommendation process is twofold, as both new activities, as well as fitness buddies, are being recommended to each user

PRO-Fit: Exercise with friends

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\u27s activity, synchronizes with the user\u27s calendar, recommends personalized workout sessions based on the user\u27s preferences, fitness goals, and availability. Moreover, PRO-Fit integrates with the user\u27s social network and recommends “fitness buddies” with similar preferences and availability