ActiveNet: A computer-vision based approach to determine lethargy

TL;DR

ActiveNet is a computer vision system that detects lethargy levels from single images to promote physical activity, applicable in various settings like online classes and security.

Contribution

It introduces a novel pose encoding method combined with machine learning for real-time lethargy detection from monocular images.

Findings

Effective real-time lethargy detection from single images

Potential applications in health, security, and online education

Alert system to motivate activity

Abstract

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.