  The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a
significant drop in physical activeness. Our work is constructed upon the idea
to formulate a backbone mechanism, to detect levels of activeness in real-time,
using a single monocular image of a target person. The scope can be generalized
under many applications, be it in an interview, online classes, security
surveillance, et cetera. We propose a Computer Vision based multi-stage
approach, wherein the pose of a person is first detected, encoded with a novel
approach, and then assessed by a classical machine learning algorithm to
determine the level of activeness. An alerting system is wrapped around the
approach to provide a solution to inhibit lethargy by sending notification
alerts to individuals involved.
