Design of artificial intelligence auxiliary system for elderly care service based on big data analysis
Qi Chen, Nan Sheng · 2024
In order to make the elderly care service system intelligent, this paper proposes a human posture recognition algorithm based on big data analysis and artificial intelligence technology. The algorithm framework includes three parts: human target detection, human posture estimation and human action recognition. Firstly, this paper adopts the obscured human body detection algorithm based on the mixed attention mechanism to detect the human bounding box. Then, to solve the problem that the detected human bounding box may have inaccurate positioning leading to the human body position deviation, the space transformation network is used to affine the coordinates of the human bounding box to correct its position. Finally, the stacked hourglass network is used to predict the joints of the detected human body, so as to realize the recognition of human posture. In this paper, the algorithm is trained and tested on the open pedestrian test set. The experimental results show that the algorithm in this paper can effectively and correctly estimate the visible parts of human posture, and reduce the influence of obstacle occlusion on human posture estimation and human fall detection. The evaluation index of human posture recognition (Kps AP) reached 68.7%, indicating that the posture recognition algorithm proposed in this paper can realize the rapid recognition and estimation of human posture without shelter, and the algorithm can complete the fall recognition of the elderly in the elderly service system, and can effectively prevent the elderly from falling after the subsequent big data statistics and analysis of the elderly's posture. It has good practicability.