Blood pressure (BP) is one of the essential indicators of human health and can be greatly influenced by lifestyle factors (e.g., activity and sleep). However, the degree of impact of each lifestyle factor on BP is unknown and may vary significantly between individuals. Utilizing data remotely collected by home BP monitors and wearable activity trackers, we aim to use machine learning techniques to investigate the complex relationships between BP and lifestyle factors in order to provide personalized and proactive hypertension care. In addition, we investigate continuous, non-invasive BP measurement using the photoplethysmogram (PPG) sensor which has become widely accessible in wearables and medical devices. Our clinical trial with UCSD Health demonstrates prospects for reducing BP through precise lifestyle changes, either effectuated through an interactive lifestyle coach with precise recommendations.
Personalized Blood Pressure Estimation Using Photoplethysmography
