Publication

Abstract : People must incorporate a “work from home” strategy because of the COVID-19 outbreak. In today's pandemic situation, due to working from home, employees are working for long hours, and spending long hours is a pretty challenging task. Nowadays, irrespective of age concerns, sciatica, Kyphosis, and lordosis are becoming a significant problem even for youngsters. The longest nerve in our body is sciatica, which causes severe pain due to stress applied while sitting in the wrong posture. It gets compressed with our lower back discs, which may lead to severe radiating pain from our lower back disc to the entire right leg, and a person can't even perform his daily activities comfortably. To prevent these problems, sitting posture while working should be maintained correctly. This work mainly focuses on preventing employers, and students from sciatica, Kyphosis, and Lordosis health issues. We used all kinds of sitting postures that interact while working with a laptop, classified which posture was good, and predicted which stance led to health issues. We used Convolutional Neural Network and K-Nearest Neighbor machine learning algorithms to predict the correct sitting postures. In KNN, we followed two techniques to improve the performance: using Edge detection. The other method we used was detecting facial landmark detection and plotting their respective rotational angles. So by using this technique, we improved the accuracy and precision rate compared to the classical Edge detection. We also trained the model with CNN, which gives good results. We performed a comparative analysis to pick the best model to integrate with OpenCV to make it real-time.