Continuous monitoring of eating and sleeping behaviors in the home environments of older adults: a case study demonstration

Abstract

Accurate observation of patient functioning is necessary for rigorous clinical research and for improving the quality of patient care. However, clinic or laboratory environments systematically differ from the contexts of everyday life. Further, assessments that are completed in a single institutional session may not be generalizable. Here, we describe a computer vision methodology that measures human functioning continuously in the environments where patients live, sleep, and eat.

Keywords: activities of daily living; aging in place seniors; artifical inteligence; computer vision; digital health.