  Recommender systems have been successfully used in many domains with the help
of machine learning algorithms. However, such applications tend to use
multi-dimensional user data, which has raised widespread concerns about the
breach of users privacy. Meanwhile, wearable technologies have enabled users to
collect fitness-related data through embedded sensors to monitor their
conditions or achieve personalized fitness goals. In this paper, we propose a
novel privacy-aware personalized fitness recommender system. We introduce a
multi-level deep learning framework that learns important features from a
large-scale real fitness dataset that is collected from wearable IoT devices to
derive intelligent fitness recommendations. Unlike most existing approaches,
our approach achieves personalization by inferring the fitness characteristics
of users from sensory data and thus minimizing the need for explicitly
collecting user identity or biometric information, such as name, age, height,
weight. In particular, our proposed models and algorithms predict (a)
personalized exercise distance recommendations to help users to achieve target
calories, (b) personalized speed sequence recommendations to adjust exercise
speed given the nature of the exercise and the chosen route, and (c)
personalized heart rate sequence to guide the user of the potential health
status for future exercises. Our experimental evaluation on a real-world Fitbit
dataset demonstrated high accuracy in predicting exercise distance, speed
sequence, and heart rate sequence compared to similar studies. Furthermore, our
approach is novel compared to existing studies as it does not require
collecting and using users sensitive information, and thus it preserves the
users privacy.
