Prolonged sitting detection for office workers syndrome prevention using kinect

This research has focused on detection of prolonged sitting of office workers by performing data mining classification on the real-time skeleton data stream captured by a single Kinect camera set up in an office worker's work station area. The system classifies the input stream into sequences of stills or moves. The performance of several classification methods such as decision tree, neural network, naive Bayes, and k-Nearest Neighbors are compared in order to acquire the optimal classifier. The proposed system can effectively monitor the user's postures with 98% accuracy and give the user real-time feedback based on the three levels of healthy in ergonomics. In addition, the proposed work includes development of an alerting device using a microcontroller, and provision of data visualization for a daily summary report.