Human activity recognition based on improved convolutional neural network

Shihao Yang; Chao Zhang; Peisi Zhong; Jing Meng; Mei Liu; Fengju Hu

doi:10.1117/12.3031216

19 July 2024 Human activity recognition based on improved convolutional neural network

Shihao Yang, Chao Zhang, Peisi Zhong, Jing Meng, Mei Liu, Fengju Hu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318112 (2024) https://doi.org/10.1117/12.3031216
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In this paper, an human activity recognition model named Improved Time-Dependence Convolutional Neural Network is proposed to improve the human-robot interaction performance of limb exoskeleton robots. Firstly, select the time series signal of human lower limb knee joint as data set, use the One-Dimensional Convolutional Neural Network to extract the motion signal feature and reduce the dimension. Then, the Gate Recurrent Unit is combined to learn the long-term time dependence of motion signals and the relationship between potential features and target output. Finally, Residual network unit is introduced to help train the deep network and improve the stability of the network training process. Built the relevant model and compared their recognition performance. The results shown that the recognition rate of ITD-CNN recognition network model for different human activities is more than 99.97%, which has a high application value in the recognition of human activity state in the exoskeleton robot field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shihao Yang, Chao Zhang, Peisi Zhong, Jing Meng, Mei Liu, and Fengju Hu "Human activity recognition based on improved convolutional neural network", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318112 (19 July 2024); https://doi.org/10.1117/12.3031216

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