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The outbreak of COVID-19 has forced everyone to stay indoors, fabricating asignificant drop in physical activeness. Our work is constructed upon the ideato 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 generalizedunder many applications, be it in an interview, online classes, securitysurveillance, et cetera. We propose a Computer Vision based multi-stageapproach, wherein the pose of a person is first detected, encoded with a novelapproach, and then assessed by a classical machine learning algorithm todetermine the level of activeness. An alerting system is wrapped around theapproach to provide a solution to inhibit lethargy by sending notificationalerts to individuals involved.

ActiveNet: A computer-vision based approach to determine lethargy