Events

The future of healthcare lies in delivering comprehensive medical services to patients in their own homes. As the global population ages and chronic diseases become increasingly prevalent, objective, longitudinal and reliable health assessment at home becomes crucial for early detection and prevention of hospitalization. Advances in machine learning and smart sensors hold immense potential for transforming in-home healthcare. However, enabling these technologies for at-home clinical applications requires addressing several challenges, including discovering effective biomarkers with accessible vitals, learning from real-world sparse and biased health data, and making ML algorithms reliable for deployment across diverse environments and populations.

In this talk, I will present new learning methods with everyday devices for in-home healthcare that address these challenges. I will introduce an AI-powered digital biomarker for Parkinson’s disease that detects the disease, estimates its severity, and tracks its progression using nocturnal breathing signals. Furthermore, I will showcase the potential of AI-based in-home assessment for various diseases and human health sensing, enabling remote monitoring of health-related conditions, timely care and enhancing patient outcomes.