人体活动识别数据集的数据处理方法

doi:10.16039/j.cnki.cn22-1249.2020.03.017

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摘要

对由可穿戴设备采集的针对人体活动识别的数据集进行处理，并用处理后的数据对一维卷积神经网络进行训练，测试并得到精准度结果。对数据集的处理使得原本数据集中一些噪音和无效数据被过滤排除掉,在训练神经网络时减少了运算量，提升了神经网络的效率。后经测试，在神经网络结构不变的情况下，处理后的数据集可以使神经网络性能得到提升。

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	钟楚轶
	朱建军

关键词: 数据集处理卷积神经网络人体活动识别

Abstract:

In this paper, the data set for human activity recognition collected by the wearable device is processed, and the processed data is used to train a one-dimensional convolutional neural network to test and obtain accuracy results. The processing of the data set allows some noise and invalid data in the original data set to be filtered out, which reduces the amount of calculation and improves the efficiency of the neural network when training the neural network. After testing, under the condition that the structure of the neural network is unchanged, the processed data set can improve the performance of the neural network.

Key words: dataset processing concolutional neural network human activity recognitio

出版日期: 2020-03-25 发布日期: 2020-03-25 整期出版日期: 2020-03-25

ZTFLH:

TP391

引用本文:

钟楚轶, 朱建军. 人体活动识别数据集的数据处理方法 [J]. 吉林化工学院学报, 2020, 37(3): 81-84.
ZHONG Chuyi, ZHU Jianjun. Human Activity Recognition based on Wearable Sensor Dataset and Convolutional Neural Network . Journal of Jilin Institute of Chemical Technology, 2020, 37(3): 81-84.

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http://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2020.03.017 或 http://xuebao.jlict.edu.cn/CN/Y2020/V37/I3/81

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