Smart homes are the most important sustainability technology of our future. In smart homes, intelligent monitoring is an important component. However, there is currently no effective method for human posture detection for monitoring in smart homes. So, in this paper, we provide an infrared human posture recognition method for monitoring in sustainable smart homes based on a Hidden Markov Model (HMM). We also trained the model parameters. Our model can be used to effectively classify human postures. Compared with the traditional HMM, this paper puts forward a method to solve the problem of human posture recognition. This paper tries to establish a model of training data according to the characteristics of human postures. Accordingly, this complex problem can be decomposed. Thereby, it can reduce computational complexity. In practical applications, it can improve system performance. Through experimentation in a real environment, the model can identify the different body movement postures by observing the human posture sequence, matching identification and classification process. The results show that the proposed method is feasible and effective for human posture recognition. In addition, for human movement target detection, this paper puts forward a human movement target detection method based on a Gaussian mixture model. For human object contour extraction, this paper puts forward a human object contour extraction method based on the Sobel edge detection operator. Here, we have presented an experiment for human posture recognition, and have also examined our cloud-based monitoring system for elderly people using our method. We have used our method in our actual projects, and the experimental results show that our method is feasible and effective.
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