Advances in artificial intelligence-based autonomous applications have led to the advent of domestic robots for smart elderly care; the preliminary critical step for such robots involves increasing the comprehension of robotic visualizing of human activity recognition. In this paper, discrete hidden Markov models (D-HMMs) are used to investigate human activity recognition. Eleven daily home activities are recorded using a video camera with an RGB-D sensor to collect a dataset composed of 25 skeleton joints in a frame, wherein only 10 skeleton joints are utilized to efficiently perform human activity recognition. Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. The novelty indicated D-HMM theory is not only promising in terms of speech signal processing but also is applicable to visual signal processing and applications.

In recent years, the sustainable development of education has become an increasing concern, and new technology characterized by intelligence has played an important role in promoting it. However, facing the endless stream of teaching platforms, learning platforms, student management platforms and learning APPs, teachers and students are tired of coping. Meanwhile, there has been a serious lack of real information about student growth, especially in the fields of electronic graphics, and audio and video materials. At present, there is no continuous student growth system that can be used over the course of their life, which is very unfavorable to their individual development. Graphic code is a technology with the potential to solve these problems; however, the existing graphic code technology suffers from clear deficiencies in the realization of a personalized student growth system that incorporates intelligence, security and sustainability. In response to this, this paper proposes a new generation of graphic code technology, namely intelligent graph element technology (IGET). Further, a new sustainable personalized student growth system model is designed based on artificial intelligence, big data analysis and intelligent graph element technologies, and the architecture and implementation of this system platform are completed. Finally, a student growth system based on intelligent code is verified through by an analysis of the results of a questionnaire survey. The research results show that, compared with the traditional student management system, the student growth system based on an intelligent graph element code has obvious advantages in convenience, intelligence, precision, security, and sustainability.