Passive longitudinal weight and cardiopulmonary monitoring in the home bed

Nicholas Harrington,Parag Agnihotri,Pamela N Deyoung,Zhe Wei,Brandon Hernandez-Pacheco,Deepak L Bhatt,Andrew Wassell,Robert L Owens,Kevin R King,Todd P Coleman,Bayan Duwaik,Akshay S Desai,Quan M Bui

doi:10.1038/s41598-021-03105-1

Abstract

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.

Highlights

Remote monitoring technologies, we developed a non-invasive, multiparameter measurement technology that passively collects total body weight and dynamic cardiopulmonary physiology in the home bed without requiring any active patient participation
Passive home monitoring has been achieved by embedding sensors into a wide range of everyday objects, facilitating measurement of respiratory signals and ballistocardiograms (BCG, a mechanical cardiac signal correlated with stroke volume and contractility) from healthy individuals and patients with chronic d iseases[17,18,19,20,21]
This is a unique setting in which cardiopulmonary physiology can be longitudinally measured without requiring correction for level of activity and without obscuration by musculoskeletal movements. To leverage this ideal diagnostic window, we designed, manufactured, and validated a non-contact fully adherence-independent home monitoring system called BedScales. It consists of low-profile force sensors beneath each leg of a conventional home bed, recliner, or couch, which measure and transmit 80 Hz sampled data to a cloud computing environment via WiFi, where physiological parameters and signals are quantified including total body weight, detailed respiratory waveforms, ballistocardiograms, and musculoskeletal movements, all without requiring any conscious patient participation, management of devices, engagement with apps, or self-application of wearables (Fig. 1a)

Summary

Introduction

Remote monitoring technologies, we developed a non-invasive, multiparameter measurement technology that passively collects total body weight and dynamic cardiopulmonary physiology in the home bed without requiring any active patient participation. Passive home monitoring has been achieved by embedding sensors into a wide range of everyday objects, facilitating measurement of respiratory signals and ballistocardiograms (BCG, a mechanical cardiac signal correlated with stroke volume and contractility) from healthy individuals and patients with chronic d iseases[17,18,19,20,21]. We developed an under-the-bed mechanical sensing platform (“BedScales") that achieves adherence-independent noncontact longitudinal physiological monitoring of total body weight, high fidelity respiratory signals, and BCG-derived heart rates. By eliminating the need for patient participation, BedScales has the potential to improve care of patients with difficult-to-manage chronic cardiopulmonary diseases such as heart failure

Methods

Results

Conclusion