Feature recognition in multiple CNNs using sEMG images from a prototype comfort test

Objective: Deep learning-based CNN networks have recently been investigated to solve the problem of body posture recognition based on surface electromyographic signals (sEMG). Influenced by these studies, to develop a combined approach of sEMG and CNNs in the study of human-product interactions and the impact of body comfort, and to compare the advantages and disadvantages of various CNNs networks.Methods: In this study, sEMG measurements were carried out by building a prototype usability experiment, and the data were divided into four categories, with two types of datasets: training and testing. Four CNNs, LeNet-5, VGGNet-11, InceptionNet V4, and DenseNet, were used for the recognition of sEMG images.Results: DenseNet is another type of convolutional neural network with deep layers, which has a unique advantage over other algorithms. unique advantages over other algorithms. DenseNet has fewer layers and better accuracy than InceptionNet V4, but not only does it bypass enhanced feature reuse, but its network is easier to train and has some regularization effects, while also mitigating the problems of gradient disappearance and model degradation.Conclusion: These findings could lead to a more appropriate CNN model and a useful tool for developing comfort judgments of surface EMG signals, furthering the development of products that come into contact with the human body without the need for routine retraining.

Automated summary

This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.