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.