Federated learning enhanced AR/VR integration in sleep wearable devices

Abstract

As a crucial aspect of human health, sleep remains a problem to many individuals. Many still struggle with going to sleep or poor sleeping quality. In terms of ways to interfere with this issue, traditional sleep measurement methods, such as polysomnography(PSG), are intrusive, expensive, and inconvenient for long-term monitoring. Wearable devices offer a practical alternative but often face challenges related to accuracy, comfort, and data privacy. Advances in technology present new opportunities to address these issues.This systematic review focuses on examining the drawbacks of current sleeping measurements and wearable devices, and further explores the integration of federated learning (FL) with AR, VR in sleep wearable devices in order to promote sleep facilitation. The study addresses key challenges in sleep research, including accurate and non-intrusive monitoring, data privacy, and personalized sleep coaching. Federated learning enables decentralized data analysis, enhancing model accuracy while preserving user privacy. AR and VR technologies offer immersive environments for sleep facilitation through tailored relaxation techniques. The combination of these technologies provides innovative solutions to improve sleep quality and intervention efficacy. Future research should focus on enhancing device comfort, accuracy, and the integration of FL algorithms for better user experiences.