基于空间特征的无线体域网人体姿态识别算法

doi:10.11959/j.issn.2096-3750.2019.00121

Abstract

Abstract:

Traditional image-based activity recognition algorithms have some problems,such as high computational cost,numerous blind spots and easy privacy leakage.To solve the problem above,the CCLA (convolution-convolutional long short-term memory-attention) activity recognition algorithm based on the acceleration and gyroscope data was proposed.The convolutional neural network was used to extract spatial features of activity data and got the hidden time series information from the convolutional long short-term memory network.Simulating human brain selecting attention mechanism,attention-encoder was constructed to extract the spatial and temporal features at a higher level.The CCLA algorithm was tested on UCI-HAPT (university of California Irvine-smartphone-based recognition of human activities and postural transitions) public data set,and realized the classification of 12 types of activity with the accuracy of 93.27%.

Key words: neural network, activity recognition, attention mechanism, wireless body area network (WBAN)

CLC Number:

TP183

Chi JIN,Zhijun LI,Dayang SUN,Fengye HU. Human activity recognition algorithm based on the spatial feature for WBAN[J]. Chinese Journal on Internet of Things, 2019, 3(3): 70-75.

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References 19

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层数	层类型	输出尺寸	参数量/个
1	Conv	(None,8,100,6)	136
2	MaxPooling	(None,8,100,6)	0
3	Conv	(None,8,100,6)	1 032
4	MaxPooling	(None,8,100,6)	0
5	Conv	(None,10,100,6)	1 290
6	Reshape	(None,10,10,10,6)	0
7	Permute	(None,10,10,10,6)	0
8	ConvLSTM	(None,10,8,10,6)	5 216
9	ConvLSTM	(None,10,10,10,6)	6 520
10	Permute	(None,10,10,10,6)	0
11	Reshape	(None,100,60)	0
12	Dense	(None,100,196)	11 956
13	Permute	(None,196,100)	0
14	Dropout	(None,196,100)	0
15	Attention	(None,100)	296
16	Dense	(None,12)	1 212
总参数			27 658

分类器	姿态						准确率
分类器	坐姿	站姿	躺姿	行走	下楼	上楼	准确率
MC-SVM	95.6	97.1	100	87.2	72.6	87.2	89.3%
MCHF-SVM	94.7	97.8	100	87.2	74.0	81.7	89.0%
CCLA	97.0	95.4	97.3	88.9	93.5	98.1	90.2%

Human activity recognition algorithm based on the spatial feature for WBAN

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