Detecting medical status and cognitive impairment utilizing ambient data

Abstract

Methods and systems for determining the likelihood that a person is impaired, generally comprising gathering ambient data from sensors operably coupled to a primary measuring device (PMD), analyzing and/or identifying the ambient data, querying at least one database to identify baseline data regarding the person, and comparing the ambient data to the baseline data to determine the likelihood that the person is impaired. The methods and systems advantageously detect medical and/or cognitive impairment without the need for specialized devices, examination by specialists, consent of the person being evaluated, and compliance with recommendations to be examined, and may be applied to estimating the level of impairment of a person due to alcohol, drugs, exposure to toxic chemicals, changes to mental status, insufficient sleep, medical problems, changes to blood sugar levels, divided attention, vision changes, hearing changes, use of a mobile device while walking or operating a vehicle, and other factors.