From 7e6f478d5c0ad2e53f8741d500c081b0f0377c8a Mon Sep 17 00:00:00 2001 From: Valeria Graziano Date: Thu, 29 Sep 2022 05:15:40 -0700 Subject: [PATCH] !publish! --- content/factor/stillnotrobots.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/factor/stillnotrobots.md b/content/factor/stillnotrobots.md index 490304a..008e169 100644 --- a/content/factor/stillnotrobots.md +++ b/content/factor/stillnotrobots.md @@ -72,7 +72,9 @@ As the grandaughters of that 15 year old girl, we do not know different working ![](static/images/ amazon_cage.png) +>Now a days, employee health and well-being is the most important consideration in the work place. Because it will affect the productivity of an individual employee and team contribution. Eventually, the automatic facial expression analysis using machine learning has become an interesting and active research area from past few decades.In this paper, Real time Employee Emotion Detection System (RtEED) has been proposed to automatically detect employee emotions in real time using machine learning. RtEED system helps the employer can check well-being of employees and identified emotion will be intimated to respective employee through messages. Thereby employees can make better decisions, they can improve their concentration level towards work and adopt to the healthier life style and much productive work styles. CMU Multi-PIE Face Data is used to train machine learning model. Each employee will be equipped with a webcam to capture facial expression of an employee in real time. The RtEED system designed to identify six emotions such as happiness, sadness, surprise, fear, disgust and anger through the captured image. Results demonstrate that expected objectives are achieved. +- from: K. S. Chandraprabha, A. N. Shwetha, M. Kavitha and R. Sumathi, [Real time-Employee Emotion Detection system (RtEED) using Machine Learning](https://ieeexplore.ieee.org/abstract/document/9388510), 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021, pp. 759-763, doi: 10.1109/ICICV50876.2021.9388510. # How are we? On the degradation of planetary health