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Vision based human pose estimation is an non-invasive technology for Human-Computer Interaction (HCI). The direct use of the hand as an input device provides an attractive interaction method, with no need for specialized sensing equipment, such as exoskeletons, gloves etc, but a camera. Traditionally, HCI is employed in various applications spreading in areas including manufacturing, surgery, entertainment industry and architecture, to mention a few. Deployment of vision based human pose estimation algorithms can give a breath of innovation to these applications. In this article, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module. Our proposed model can be deployed on an embedded system due to its lightweight nature with just 1.9 Million parameters. The source code and qualitative results are publicly available.