# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 1 Introduction  
With the rapid development of vision technology and artificial intelligence, people have higher requirements for human-computer interaction. Compared with the traditional human-computer interaction, the interaction based on human biological characteristics is simpler and more flexible. Gesture, as the second language of human beings, has rich meaning. Gesture recognition has become a research hotspot in the field of human-computer interaction [1]. It is understood that there are three main categories of static gesture recognition technologies based on monocular vision [2]: the first is template matching technology, which matches the feature parameters of the gesture to be recognized with the template feature parameters stored in advance. It then completes the recognition task by measuring the similarity between them. The second is statistical analysis technology, a classification method based on probability and statistics theory that determines the classifier by a statistical sample feature vector. This technology requires people to extract specific feature vectors from the original data and classify these feature vectors instead of identifying the original data directly. The third type is a neural network technology, which has the ability of self-organization and self-learning, the characteristics of distribution, the ability of pattern generalization, and can effectively resist noise and deal with incomplete patterns. In the preparatory work, this paper used a Caffe [3, 4] framework to train the AlexNet [5, 6, 7] network and carried out 180K iterative training by optimizing solver parameters. The convolution full connection process is shown in Figure 1. In the process of training, thispaper used the hold-out method to divide the data set into a training set and testing set. The size of the training set was 18,000 pictures (each type of gesture). The size of the testing set was 4000 pictures (each type of gesture) and the input size of each picture was \(227\times 227\times 3\). A total of 7 types of gestures were trained. However, the recognition results are not satisfactory. The gesture recognition rate of many elderly people is below 80%. If this algorithm is applied to the elderly-accompanying robot, the robot will not be able to provide services for the elderly.  
Therefore, this paper proposes a gesture error correction algorithm for the elderly-care robot. The purpose is to improve the recognition rate of the robot, improve the service quality of the elderly-care robot, and improve the elderly-care experience. Firstly, based on the behavior of the elderly, the recognition rate of hand gestures in natural interaction is calculated, a misread-gesture database is established, and the causes of recognition errors are analyzed. Then, based on the misread-gesture database, the reason for the low recognition rate of gestures based on the robot cognitive layer is explored. With the help of curve fitting and the Pearson correlation coefficient algorithm, we calculate the channel layer number with the biggest difference in the fifth layer feature map of convolution layer among different gesture categories in the gesture library. Finally, on this basis, a game rule correction algorithm is established.