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In order to solve the problems of low efficiency and complex operation in traditional nursing beds, this study investigates gesture detection through feature pyramids, and introduces channel attention and Transformer attention to optimize the feature pyramids. Based on Raspberry Pi, a nursing bed control system is designed, and the optimized feature pyramids are applied to it, thus designing an intelligent control system for nursing beds that combines feature pyramid networks with Raspberry Pi. The results show that the accuracy of the improved model on the COCO dataset can reach 95%. In the angle testing error, the minimum error rate of the improved model is 1.17%, which proves its high accuracy. In terms of recognition rate and average testing time, the highest recognition rate of the improved model in different instructions can reach 98.7%, and the average testing time is 0.18 seconds, which proves its effectiveness and further proves its accuracy. The intelligent nursing bed based on this control method can effectively improve the nursing quality and comfort of the elderly, providing a new direction for the further development of intelligent nursing beds.