Real-Time Tracking and Recognition Systems for Interactive Telemedicine Health Services

Recent changes affecting the health industry include the digitization of medical information as well as the exchange of medical information through a network-connected medical infrastructure. In this paper, we propose a real-time tracking and recognition system for interactive telemedicine health services. The proposed method is a methodology for both hand and finger detection applied to posture recognition in telemedicine. The detected hand or finger can be used to implement a non-contact mouse in the machine-to-machine. This technology can be used to control telemedicine health devices such as a public healthcare system, pedometer health information reader, glucose-monitoring device, and blood pressure gauge. Skin color is used to segment the hand region from the background, and the contour is extracted from the segmented hand. Contour analysis provides the locations of the fingertips on the hand. Fingertip tracking is performed using a constant velocity model with a pixel-labeling approach. From the tracking process, several hand features can be extracted and then fed into a finite state classifier to identify the hand configuration. The hand can be classified into many gesture classes or several different movement directions. Using this method, we performed an extensive experiment and obtained a very encouraging result. It is shown that using the method used in previous studies, some of the points are lost, whereas using the proposed method described in this paper, all lost points are recovered with no or little displacement error. Ultimately, this paper provides empirical verification of the adequacy and validity of the proposed system for telemedicine health services. Accordingly, the satisfaction and quality of services will improve gesture recognition for interactive telemedicine health services.