Design of HAR data acquisition system based on inertial sensor

Home > Archive>Volume 46, Issue 23, 2023 >146-152

Design of HAR data acquisition system based on inertial sensor
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                        Wang YingWang Ying
1.College of Information Engineering, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China； 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China
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Yang ZhijiaYang Zhijia
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China； 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China
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Xie ChuangXie Chuang
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China； 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China
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Zeng Jing1Zeng Jing
College of Information Engineering, Shenyang University of Chemical Technology，Shenyang 110142, China
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Wang BinyuWang Binyu
1.College of Information Engineering, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China； 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China
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Affiliation:1.College of Information Engineering, Shenyang University of Chemical Technology，Shenyang 110142, China; 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences，Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences，Shenyang 110016,China
Clc Number:TN96；TP212.9；TP368
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Abstract:

Currently, research on data acquisition hardware systems for human activity recognition is limited, and there is a problem of a lack of diverse and generalized reference datasets. In this paper, we design a low-power data acquisition system that supports real-time data transmission and propose a data acquisition method with randomness and crossover. Firstly, a low-power acquisition platform is built for data acquisition, wireless transmitting and receiving, and pre-processing; secondly, a comprehensive and accurate data acquisition scheme is developed to improve the generalization of the new dataset; and finally, a 2D-CNN neural network is used to train a model for the acquired dataset in different modes. The experimental results demonstrate that the designed data acquisition system has a reasonable structure and low power consumption, ensuring real-time data transmission. The application of this system greatly improves the quality of the dataset. The obtained dataset achieves an accuracy of 92.54% on deep learning models. Compared to traditional datasets, the new dataset shows significantly better performance in human activity recognition tasks. The development of this data acquisition system and dataset provides convenience for neural network applications.

Key words:inertial sensor;data acquisition;Wi-Fi wireless module;human activity recognition

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Online: March 21,2024
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