Patient necessity notification system based on gesture recognition (Kinect V2) and internet of things using selection frame method

Abstract

The use of gesture recognition integrated with Internet of Things (IoT) as a system for detecting patient needs provides information speed and accuracy for nurses. One tool that is widely used to implement gesture recognition is Kinect (Version 2). For data communication, Kinect will be collaborated with IoT in the process of sending data from the start after gesture recognition to the presentation of information to end users. The problem that arises when Kinect is integrated with IoT is the burden on the network because the average number of frames read by Kinect in one second is 30 frames. In this study, a frame selection method was developed to minimize the number of frames sent to IoT networks by minimizing the number of duplicate frames. The selection frame method on gesture recognition integrated with IoT is then tested for performance with distance and delay parameters. The system implementation results are able to read 100% gesture recognition, and the accuracy of the information sent by the nurse is 100% in the testing environment. The optimal distance that can provide a reading of more than 95% is 2.5 meters. The optimal value of 100% is not achieved because some testing of the falling position cannot read the whole skeleton.