A rehabilitation activity monitoring method based on Shallow-CNN

Sisi Wu; Tianyu Huang; Yihao Li

doi:10.1109/BIBM55620.2022.9995387

A rehabilitation activity monitoring method based on Shallow-CNN

Sisi Wu, Tianyu Huang^*, Yihao Li

^*此作品的通讯作者

计算机学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
编辑	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
出版商	Institute of Electrical and Electronics Engineers Inc.
页	2482-2489
页数	8
ISBN（电子版）	9781665468190
DOI	https://doi.org/10.1109/BIBM55620.2022.9995387
出版状态	已出版 - 2022
活动	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, 美国期限: 6 12月 2022 → 8 12月 2022

出版系列

姓名	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

会议

会议	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
国家/地区	美国
市	Las Vegas
时期	6/12/22 → 8/12/22

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/BIBM55620.2022.9995387

其它文件与链接

链接到 Scopus 的出版物

引用此

Wu, S., Huang, T., & Li, Y. (2022). A rehabilitation activity monitoring method based on Shallow-CNN. 在 D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (编辑), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (页码 2482-2489). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995387

Wu, Sisi ; Huang, Tianyu ; Li, Yihao. / A rehabilitation activity monitoring method based on Shallow-CNN. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. 编辑 / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. 页码 2482-2489 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@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",

}

Wu, S, Huang, T & Li, Y 2022, A rehabilitation activity monitoring method based on Shallow-CNN. 在 D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (编辑), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., 页码 2482-2489, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, 美国, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995387

A rehabilitation activity monitoring method based on Shallow-CNN. / Wu, Sisi; Huang, Tianyu; Li, Yihao.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. 编辑 / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. 页码 2482-2489 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A rehabilitation activity monitoring method based on Shallow-CNN

AU - Wu, Sisi

AU - Huang, Tianyu

AU - Li, Yihao

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - HAR

KW - attention

KW - deep learning

KW - real-time

KW - wearable sensor

UR - http://www.scopus.com/inward/record.url?scp=85146659179&partnerID=8YFLogxK

U2 - 10.1109/BIBM55620.2022.9995387

DO - 10.1109/BIBM55620.2022.9995387

M3 - Conference contribution

AN - SCOPUS:85146659179

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 2482

EP - 2489

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Y2 - 6 December 2022 through 8 December 2022

ER -

Wu S, Huang T, Li Y. A rehabilitation activity monitoring method based on Shallow-CNN. 在 Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, 编辑, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. 页码 2482-2489. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995387

A rehabilitation activity monitoring method based on Shallow-CNN

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此