Artificial Intelligence Assistive Technology in Hospital Professional Nursing Technology

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.

Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

A Framework for Users’ Attitude Prediction Based on Visual Features: Application to Movies Recommendations

Recommendation and personalisation approaches aim to filter the most interesting resources that may attract users’ personal interests and preferences by analysing their past attitudes and consumption patterns. The visual aspect of content constitutes an important factor that drives consumers’ attitudes and decisions. In this context, this work proposes a framework for users’ attitude prediction based on items’ visual descriptors and details one of its possible applications for movies recommendations. The main idea of our proposal is to model users’ interests and consumption behaviors using the movies’ posters images and extract features based on the visual descriptors of the items that they interact with, in order to better predict their attitudes towards the ones they do not know. The recommendation approach was integrated into a movies recommendation application that visually assists users while searching for relevant movies to watch, by finding similar movie posters based on the visual aspects of the poster image.