Development of Human Action Feature Recognition Using Sensors

doi:10.3778/j.issn.1002-8331.1809-0016

Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (1): 23-28.DOI: 10.3778/j.issn.1002-8331.1809-0016

Previous Articles Next Articles

Development of Human Action Feature Recognition Using Sensors

JING Yuan1，2, JI Aihong2, ZHANG Xiyuan2，3, SONG Yawei4, CHEN Weifeng1

1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.Institute of Bio-inspired Structure and Surface Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
3.College of Olympic, Nanjing Sport Institute, Nanjing 210000, China
4.Department of Sports & Health Science, Nanjing Sport Institute, Nanjing 210014, China

Online:2019-01-01 Published:2019-01-07

传感器人体运动行为特征识别研究进展

景元1，2，吉爱红2，张曦元2，3，宋雅伟4，陈炜峰1

1.南京信息工程大学自动化学院,南京 210044
2.南京航空航天大学仿生结构与材料防护研究所,南京 210016
3.南京体育学院奥林匹克学院,南京 210000
4.南京体育学院运动健康科学系,南京 211014

Abstract

Abstract: The recognition of human action feature has a widely use for medical health, man-machine interaction, sport competition and so on. Firstly, the common sensors and methods for motion data acquisition are introduced. Secondly, the general methods for motion data preprocessing are briefly described. And then, the feature extraction of motion data is introduced. Then the research status of feature recognition is pointed out, and the application status is briefly described. Finally, the problems faced in the current and the future development are put forward.

Key words: human action, recognition of feature, acceleration sensor, pressure sensor, electromyography

摘要： 人体运动行为特征识别在医疗健康、人机接口、体育竞技等诸多领域都有广泛应用。介绍了常见的用于运动数据采集的传感器和相应方法，简述了运动数据预处理的一般手段；介绍了运动数据特征提取的过程，重点描述了常见运动行为特征识别方法的研究现状，并简述其应用现状。最后，指出了目前运动行为特征识别中所面临的问题，并给出了未来的发展方向。

关键词: 人体运动行为, 特征识别, 加速度传感器, 压力传感器, 肌电信号

JING Yuan1，2, JI Aihong2, ZHANG Xiyuan2，3, SONG Yawei4, CHEN Weifeng1. Development of Human Action Feature Recognition Using Sensors[J]. Computer Engineering and Applications, 2019, 55(1): 23-28.

景元1，2，吉爱红2，张曦元2，3，宋雅伟4，陈炜峰1. 传感器人体运动行为特征识别研究进展[J]. 计算机工程与应用, 2019, 55(1): 23-28.

[1]	LI Yuanxiang, XIE Linbo. Human Action Recognition Based on Depth Motion Map and Dense Trajectory [J]. Computer Engineering and Applications, 2020, 56(3): 194-200.
[2]	SUI Xiuwu, NIU Jiabao, LI Haotian, QIAO Mingmin. Upper Limb sEMG Gesture Recognition Method Based on NMF-SVM Model [J]. Computer Engineering and Applications, 2020, 56(17): 161-166.
[3]	GE Yun1, JING Guodong2. Human Action Recognition Based on Convolution Neural Network Combined with Multi-Scale Method [J]. Computer Engineering and Applications, 2019, 55(2): 100-103.
[4]	HUAN Zhan, LI Chen, WAN Caiyan, CHEN Xuejie. Personnel Identification Based on Walking Acceleration Information Segmentation [J]. Computer Engineering and Applications, 2019, 55(1): 203-209.
[5]	XIE Xiaoyu1, LIU Zhejie1，2. Electromyography hand gesture recognition method based on DTW [J]. Computer Engineering and Applications, 2018, 54(5): 132-137.
[6]	LU Tianran, YU Fengqin, YANG Huizhong, CHEN Ying. Human action recognition based on dense trajectories with saliency detection [J]. Computer Engineering and Applications, 2018, 54(14): 163-167.
[7]	LI Xinning, ZHOU Zhiping, LIU Lele. Multi sensor information analysis of mobile phone user authentication scheme [J]. Computer Engineering and Applications, 2018, 54(1): 140-145.
[8]	QIN Fang1, SUN Ziwen1, BAI Yong2. Research on real-time fall detection system based on smartphone [J]. Computer Engineering and Applications, 2016, 52(11): 136-140.
[9]	ZHOU Boxiang, LI Ping, LI Lian. Improved random forest algorithm and its application in human posture patterns recognition [J]. Computer Engineering and Applications, 2015, 51(16): 86-92.
[10]	WANG Jiefeng, ZHOU Zhiping, MIAO Minmin. Study of DTW matching algorithm on mobile terminal gesture recognition [J]. Computer Engineering and Applications, 2015, 51(13): 197-201.
[11]	XIONG Ping, TANG Jian. Surface EMG signal preamplifier system [J]. Computer Engineering and Applications, 2015, 51(1): 218-222.
[12]	HONG Yunguo. Human action recognition based on improved Canny operator and neural network [J]. Computer Engineering and Applications, 2013, 49(8): 156-159.
[13]	GUO Yina1, WANG Qinghua1, ABRAHAM Ajith2. Real-time electromyography hand gesture recognition model based on flexible neural trees [J]. Computer Engineering and Applications, 2012, 48(17): 207-210.
[14]	YANG Zhibang1，XU Cheng1，ZHOU Xu2，TIAN Zheng1. Design and implementation of testing and validation platform for automotive airbag controller [J]. Computer Engineering and Applications, 2011, 47(27): 65-68.
[15]	FENG Zhi-gang¹，XU Tao¹，WANG Qi². Development of self-validating pressure sensor hardware platform [J]. Computer Engineering and Applications, 2010, 46(22): 71-74.

Development of Human Action Feature Recognition Using Sensors

传感器人体运动行为特征识别研究进展

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics