Pose matching and skeletal mapping method are an integral part of Augmented Reality (AR) based learning technology. In this paper a mechanism for pose matching is presented based on extraction of skeletal data from the dance trainer’s physical movements in the form of color defined images snapped by Kinect, where each pose is modelled by a sequence of key movements and continues data frames. In order to extract the exact matched pose, the frame sequence is divided into pose feature frame and skeletal data frame by the use of pose matching dance training movement recognition algorithm (PMDTMR). This proposed algorithm is compared with other published methods in terms of frame level accuracy and learning time of dance session. The experimental results show that the proposed algorithm outperforms the state of art techniques for successful identification and recognition of matched pose between the dance trainer and the expert of the pre-recorded video through the Kinect sensor.

Energy usage as it relates to its security and management is an important aspect that all organizations having a vested stake in power production should be concerned with in these coming years. In this respect, harnessing the knowledge so that it may benefit various sectors, from the power producer right down to the customers, can be achieved by way of the digital energy hub, as proposed in this paper. We first put into context the workings and functions of such a digital platform, and then proceed to outline how it could benefit the many parties involved. Lastly, we take into account the challenges that needs to be addressed for its successful implementation, in which information security and privacy is a top priority amongst other issues.