A Machine Learning Based Multimodal Framework for Continuous Health Monitoring using Wearable and Cough Data

Abstract

The contemporary digital health environment features an abundance of applications and wearable technologies capable of monitoring a wide array of physiological parameters, including cardiac activity, energy expenditure, sleep architecture, stress levels, and critical vital signs such as ECG, SpO₂, and body temperature. However, specialized instruments, exemplified by LCM for respiratory symptom surveillance like cough, typically function in isolation from broader health tracking ecosystems. This technological proliferation has paradoxically created a significant lacuna in the integration of these disparate data sources into a cohesive, publicly accessible, and user-friendly platform. This paper presents a novel framework designed to address this challenge by employing Generative AI (GenAI) to synchronize data from discrete cough monitoring tools with wearable health device datasets. Through GenAI's capacity for multimodal data analysis, the system can discern intricate patterns and correlations between respiratory symptoms and other physiological metrics, thereby facilitating the early detection of nascent health conditions. The proposed solution is engineered to demystify health data interpretation for non-expert users by generating personalized, localized summaries and actionable insights. This integrated approach not only augments the accuracy of health assessments but also empowers individuals to exercise informed agency over their well-being. The framework holds substantial promises for advancing chronic disease management, expediting illness detection, bolstering preventive care, and informing post-care decisions, fostering a unified, intelligent, adaptive, and accessible paradigm for health monitoring [21]