Abstract
With the development of deep learning, gesture recognition systems based on the neural network have become quite advanced, but the application effect in the elderly is not ideal. Due to the change of the palm shape of the elderly, the gesture recognition rate of most elderly people is only about 70%. Therefore, in this paper, an intelligent gesture error correction algorithm based on game rules is proposed on the basis of the AlexNet. Firstly, this paper studies the differences between the palms of the elderly and young people. It also analyzes the misread gesture by using the probability statistics method and establishes a misread-gesture database. Then, based on the misreading-gesture library, the maximum channel number of different gestures in the fifth layer is studied by using the similar curve algorithm and the Pearson algorithm. Finally, error correction is completed under the game rule. The experimental results show that the gesture recognition rate of the elderly can be improved to more than 90% by using the proposed intelligent error correction algorithm. The elderly-accompanying robot can understand people’s intentions more accurately, which is well received by users.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference32 articles.
1. Research on Gesture Recognition Technology Based on Computer Vision;Qiannan,2020
2. Review of visual gesture recognition;Yi;Comput. Sci.,2016
3. A deep learning network‐assisted bladder tumour recognition under cystoscopy based on Caffe deep learning framework and EasyDL platform
4. Classification of women’s trousers silhouette using convolution neural network CaffeNet model;Wu;J. Text. Res.,2019
5. ImageNet classification with deep convolutional neural networks
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