Short-time Human Activity Recognition Based on Wavelet Features Matching

doi:10.16182/j.issn1004731x.joss.22-0176

Abstract

Abstract:

The selection of features is the key problem in the study of human activity recognition. In order to obtain sufficient and stable behavioral features, long-time behavioral data that exceed one behavior cycle are often processed, while short-time behavioral data with less than one behavioral cycle are usually unstable, making it difficult to achieve accurate and stable identification. This paper proposes a short-time human activity recognition method based on the combination of wavelet transform and template matching. Coefficient features are extracted using wavelet transform method. The features of the short-time test samples are matched with the features in the template library to make classification recognition of activity based on similarity. The experimental results show that this method has more accurate and stable recognition performance for short-time behavioral activity, which helps to realize real-time recognition of human behavioral activity.

Key words: short-time activity, wavelet transform, approximate coefficient, template matching, 1 norm

CLC Number:

TP391.4

Benyue Su, Li Zhang, Qingxuan He, Min Sheng. Short-time Human Activity Recognition Based on Wavelet Features Matching[J]. Journal of System Simulation, 2023, 35(1): 158-168.

Figures/Tables 15

Fig. 1

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Table 1

Table 2

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Table 3

User-independent experimental results

动作时长/s	特征	分类方法	平均识别率/%
动作时长/s	特征	分类方法	WARD1.0数据集	自采集数据集
大约0.13	最大值、最小值、均值、方差	K-近邻	49.30	30.12
		决策树	33.15	45.00
		支持向量机	40.85	37.88
		朴素贝叶斯	25.35	25.63
		随机森林	59.25	52.00
	小波变换的近似系数 $w$	模板匹配	78.65	71.00

Table 3

Fig. 8

Fig. 9

Table 4

User-dependent experimental results

动作时长/s	特征	分类方法	平均识别率/%
动作时长/s	特征	分类方法	WARD1.0数据集	自采集数据集
大约0.13	最大值、最小值、均值、方差	K-近邻	51.50	47.97
		决策树	33.55	49.22
		支持向量机	33.85	40.63
		朴素贝叶斯	27.35	28.13
		随机森林	53.70	55.16
	小波变换的近似系数 $w$	模板匹配	90.45	96.41

Table 4

Table 5

Table 6

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行为类别	行为描述	WARD1.0数据集	自采集数据集
上楼	上超过10阶楼梯	√	√
下楼	下超过10阶楼梯	√	√
正常行走	向前走10 s以上	√	√
顺时针转弯	顺时针转弯10 s以上	√	√
逆时针转弯	逆时针转弯10 s以上	√	√
左转弯	左转弯10 s以上	√	√
右转弯	右转弯10 s以上	√	√
慢跑	慢跑10 s以上	√	√
跳跃	原地跳超过5次	√
推轮椅	推轮椅超过10 s	√

参数	WARD1.0数据集	自采集数据集	PAMAP2数据集
动作种类	10	8	10
频率/Hz	30	96	100
传感器数量	5	5	3
特征维度	5	6	6
样本数量	1 000	320	3 500

文献	特征	方法分类	数据集	动作时长/s	识别率/%
[9]	均值, 方差, 熵谱等	支持向量机	文献[9] 数据集	5	97.10
[25]	最大值, 最小值, 均值等	卷积神经网络	WISDM	1	90.42
[25]	最大值, 最小值, 均值等	卷积神经网络	UCI	1	94.35
[26]	均值, 熵, 能量等	随机森林	文献[26]数据集	1	99.28
[27]	最大值, 最小值, 均值等	梯度增强决策树	DaLiAc	5	93.00
[10]	均值, 方差, 相关系数等	加权支持张量机	UCI	>10	89.40
本文方法	小波变换的近似系数	模板匹配	WARD1.0	0.13	90.45
			自采集数据	0.13	96.41
			PAMAP2	0.13	90.03

动作时长/s	方法	数据集	匹配时间/s	识别率/%
大约0.13	文献[28]	WARD1.0	6.94	95.15
	文献[28]	自采集数据	2.99	96.10
	本文方法	WARD1.0	1.95	90.45
	本文方法	自采集数据	0.62	96.41

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