Artificial Intelligence Assistive Technology in Hospital Professional Nursing Technology

Abstract

Global aging is becoming more and more serious, and the nursing problems of the elderly will become very serious in the future. The article designs a control system with ATmega128 as the main controller based on the function of the multifunctional nursing robot. The article uses a convolutional neural network structure to estimate the position of 3D human joints. The article maps the joint coordinates of the colour map to the depth map based on the two camera parameters. At the same time, 15 joint heat maps are constructed with the joint depth map coordinates as the centre, and the joint heat map and the depth map are bound to the second-level neural network. The prediction of the position of the user's armpit is further completed by image processing technology. We compare this method with other attitude prediction methods to verify the advantages of this research method. The research background of this article is carried out in the context of global aging in the 21st century.

Figure 1

The transfer and transportation care robot.

Figure 2

The control architecture of the transfer care robot.

Figure 3

Distribution of human joints.

Figure 4

Human pose recognition algorithm.

Figure 5

Level 2 neural network structure.

Figure 6

Comparison of average estimation accuracy.

Figure 7

Example of axillary point prediction.