Privacy-Preserving Analysis of Wearable Device Data for Monitoring Health Risks

Wearable devices play a crucial role in health monitoring by continuously collecting physiological data. However, the analysis of this sensitive data using AI poses privacy risks. This study explores the use of deep learning models for predicting symptoms from wearable data while incorporating privacy-preserving techniques. We evaluate the impact of Differentially private optimizers to ensure data confidentiality without significantly compromising model performance. We achieve a robust balance between maintaining data privacy and ensuring high predictive accuracy. Our results demonstrate that privacy-preserving techniques can be successfully implemented with minimal impact on model performance, unlocking new possibilities for secure AI-driven healthcare solutions. Furthermore, we explore the interpretability of these models, offering valuable insights into predicting anxiety through FitBit data and syncope events in POTS patients. This research opens exciting opportunities for advancing personalized healthcare, combining cutting-edge AI with strong privacy guarantees.