Machine learning based text neck syndrome detection using Microsoft Kinect sensor

Text Neck Syndrome is repeated stress injury to the neck, or it is a pain encountered in the neck due to excessive usage of handheld devices for a prolonged period. In this paper, we have used the Microsoft Kinect sensor to create the dataset for normal and abnormal sitting postures by obtaining 3D skeletal coordinates of people. Features are extracted from the data collected from the Kinect sensor data based on the various parameters like Minkowski distance, Euclidean distance, Manhattan distance, etc. Then we have applied different machine learning algorithms, namely, K-nearest neighbours, Random forest, Support vector machine, Logistic and Linear Regression, to classify them. The performance of various algorithms is analysed. The best testing accuracy achieved is 92.85% by using Random Forest and Support Vector Machine algorithms.