基于智能手机内置传感器的人体运动状态识别

doi:10.11959/j.issn.1000-436x.2019057

Abstract

Abstract:

To solve problems of low accuracy and fewer types of human motion state recognized by current smart phones,a method to do hierarchical recognition by using acceleration sensors and gravity sensors was proposed.Firstly,linear acceleration in inertial coordinate system and independent of phone direction was calculated by using the relation between acceleration and gravity acceleration.Secondly,according to the span of human motion frequency and linear acceleration vector,positions of peak and trough of footsteps were determined.Finally,feature vector of linear acceleration in time domain was extracted and human motion states were recognized hierarchically by using hierarchical support vector machine (H-SVM).The experiment shows the method can recognize six usual human motion states,while accuracy rate up to 93.37%.

Key words: motion state recognition, hierarchical support vector machine, smart phone sensor,, time domain feature

CLC Number:

TP181

Xiaoling YIN,Xiaojiang CHEN,Qishou XIA,Juan HE,Pengyan ZHANG,Feng CHEN. Human motion state recognition based on smart phone built-in sensor[J]. Journal on Communications, 2019, 40(3): 157-169.

Figures/Tables 31

References 44

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型号	CPU	内存/GB
华为mate 9	Kirin 960 8核2.4 GHz	6
小米 max2	MSM8953 8核2.0 GHz	4
OPPO A73	MT6763T 8核2.5 GHz	4
vivo X9s	MSM8976 8核1.8 GHz	4
三星C5	MSM8953pro 8核2.2 GHz	4

状态	胸口	上臂	裤前袋	裤后袋	腰部	综合
静止	97.98%	90.27%	96.59%	96.00%	98.92%	98.52%
骑行	96.39%	96.63%	92.69%	95.43%	98.62%	97.52%
跑步	96.66%	89.60%	93.51%	96.64%	97.58%	96.71%
行走	86.38%	83.29%	86.05%	86.38%	90.91%	90.03%
上楼	86.55%	82.76%	85.56%	84.59%	89.83%	88.95%
下楼	86.03%	83.22%	84.55%	84.03%	89.28%	88.51%
均值	91.66%	87.63%	89.82%	90.51%	94.19%	93.37%

状态	静止	骑行	跑步	行走	上楼	下楼
静止	7 403	92	0	12	4	3
骑行	120	8 141	10	70	3	4
跑步	0	15	11 848	218	89	81
行走	5	8	165	9 482	448	424
上楼	2	5	35	578	8 755	468
下楼	2	7	28	595	495	8 682

手机放置方式			DE-PSO算法			高斯-牛顿迭代法
X轴	Y轴	Z轴	X轴	Y轴	Z轴	X轴	Y轴	Z轴
9.810	0.000	0.000	9.794	0.015	0.015	9.733	0.040	0.049
-9.810	0.000	0.000	-9.792	0.016	0.015	-9.844	0.049	0.042
0.000	9.810	0.000	0.017	9.796	0.013	0.030	9.846	0.044
0.000	-9.810	0.000	0.016	-9.788	0.011	0.048	-9.738	0.032
0.000	0.000	9.810	0.016	0.016	9.813	0.035	0.034	9.745
0.000	0.000	-9.810	0.015	0.043	-9.815	0.049	0.049	-9.739

方法	校准前	校准后
文献[9]	90.12%	91.83%
文献[10]	91.81%	92.41%
文献[11]	90.13%	91.87%
文献[12]	91.72%	92.33%
本文	92.21%	93.37%

Human motion state recognition based on smart phone built-in sensor

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Figures/Tables 31

References 44

Related Articles 15

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位置	文献[41]	文献[42]	本文
手持(上臂)	97.00%	93.00%	96.75%
裤兜(前后)	92.00%	89.00%	97.58%
背包(腰部)	93.00%	89.50%	98.00%
上衣(胸口)	93.00%	—	97.25%
均值	93.75%	90.50%	97.39%

方法	位置数	运动状态数	特征向量	计算复杂度	准确率
文献[11]	1	6	时域、频域	O(nlbn)	96.10%
文献[12]	3	5	时域、频域	O(nlbn)	90.44%
文献[19]	1	6	时域	O(n)	91.70%
文献[44]	1	6	时域、频域	O(nlbn)	89.00%
本文	5	6	时域	O(n)	93.49%

方法	识别率
文献[11]	90.35%
文献[12]	91.28%
文献[19]	85.76%
文献[44]	84.74%
本文	93.49%

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