Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach
This work addresses privacy concerns for users of fitness apps by reducing the need for sensitive personal data, though it is incremental as it builds on existing recommender system and deep learning methods.
The paper tackles the problem of privacy breaches in fitness recommender systems by proposing a multi-level deep learning framework that uses sensory data from wearable devices to provide personalized recommendations for exercise distance, speed sequence, and heart rate sequence, achieving high accuracy on a real-world Fitbit dataset.
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.