Home health monitoring has the potential to improve outpatient management of chronic cardiopulmonary diseases such as heart failure. However, it is often limited by the need for adherence to self-measurement, charging and self-application of wearables, or usage of apps. Here, we describe a non-contact, adherence-independent sensor, that when placed beneath the legs of a patient’s home bed, longitudinally monitors total body weight, detailed respiratory signals, and ballistocardiograms for months, without requiring any active patient participation. Accompanying algorithms separate weight and respiratory signals when the bed is shared by a partner or a pet. Validation studies demonstrate quantitative equivalence to commercial sensors during overnight sleep studies. The feasibility of detecting obstructive and central apneas, cardiopulmonary coupling, and the hemodynamic consequences of non-sustained ventricular tachycardia is also established. Real-world durability is demonstrated by 3 months of in-home monitoring in an example patient with heart failure and ischemic cardiomyopathy as he recovers from coronary artery bypass grafting surgery. BedScales is the first sensor to measure adherence-independent total body weight as well as longitudinal cardiopulmonary physiology. As such, it has the potential to create a multidimensional picture of chronic disease, learn signatures of impending hospitalization, and enable optimization of care in the home.

Home health monitoring technologies promise to improve care and reduce costs, yet they are limited by the need for adherence to self-monitoring, usage of an app, or application of a wearable. While implantable sensors overcome the adherence barrier, they are expensive and require invasive procedures. Here, we describe a non-invasive, non-contact, adherence-independent sensor, that when placed beneath the legs of a patient's home bed, longitudinally monitors total body weight, detailed respiratory signals, and ballistocardiograms for months, without requiring any active patient participation.Accompanying algorithms demix weight and respiratory signals when the bed is shared by a partner or a pet. Validation studies during overnight clinical sleep studies exhibit quantitative equivalence to commercial sensors and allow discrimination of obstructive and central sleep apneas. In-home studies discriminate atrial fibrillation from normal sinus rhythm. To demonstrate real-world feasibility, we performed 3 months of continuous in-home monitoring in a patient with heart failure as he awaited and recovered from coronary artery bypass surgery. By overcoming the adherence barrier, Bedscales has the potential to create a multidimensional picture of chronic disease, learn signatures of impending hospitalization, and enable optimization of care in the home.