@inproceedings{8a18cc32c993472d9b21427fb7790bef,
title = "A rehabilitation activity monitoring method based on Shallow-CNN",
abstract = "This paper proposes a shallow convolutional neural network (CNN) model to improve the efficiency and accuracy of real-time human activity recognition (HAR). In the traditional convolutional network, an Mix-Patch-Layer (MPL) block based on the attention mechanism is added to enhance the expressiveness of the network extracted features. This block makes the features in the network focus on the information between different parts of itself, which makes up for the loss of global information in temporal data features. Experiments show that the block can improve real-time human recognition accuracy and efficiency with a shallow network.",
keywords = "HAR, attention, deep learning, real-time, wearable sensor",
author = "Sisi Wu and Tianyu Huang and Yihao Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",
year = "2022",
doi = "10.1109/BIBM55620.2022.9995387",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2482--2489",
editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, {Ananda M.} and Jin Liu",
booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",
address = "United States",
}
