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.