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Infrastructure for Machine Understanding of Video Observations in Skilled Care Facilities – Implications of Early Results from CareMedia Case Studies

CareMedia captures and analyzes a continuous audio and video record of behavior and activity in a skilled nursing facility. Through computer vision and machine learning we automatically identify individuals, classify activities, recognize behaviors, and extract relevant events. Two extensive field trials have been undertaken which produced meaningful but sometimes limited clinical results. Based on an analysis of this experience, combined with the development of new approaches and algorithms, we describe a radically improved audiovisual recording and computing infrastructure to be implemented, enabling longitudinal studies with comprehensive video and audio coverage provided with a mix of resolution, frame-rate, compression and storage requirements. Changes in the amount or rate of social interaction, eating, walking, gait, arm swing, and other attributes of motion from each patient’s baseline behavior will also be made easy to flag for professional review and diagnosis through the recording and analysis infrastructure, as such changes are key indicators to the benefits and possibly detrimental side effects of pharmacological interventions.