计算机科学 ›› 2017, Vol. 44 ›› Issue (2): 313-316.doi: 10.11896/j.issn.1002-137X.2017.02.054

• 图形图像与模式识别 • 上一篇    下一篇

基于压缩感知的移动用户行为识别方法

宋辉,王忠民   

  1. 西安邮电大学计算机学院 西安710121,西安邮电大学计算机学院 西安710121
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金(61373116),陕西省教育厅产业化培育项目(2012JC22)资助

Mobile User Behavior Recognition Based on Compressed Sensing

SONG Hui and WANG Zhong-min   

  • Online:2018-11-13 Published:2018-11-13

摘要: 为了提高移动用户行为识别的准确率,提出一种基于压缩感知的行为识别方法,其可对原始加速度数据或压缩后的加速度数据进行行为识别。依据压缩感知理论中可以由冗余字典重构数据的原理,将原始三轴加速度数据作为训练样本构造冗余字典,基于该字典求解最小l1范数得到待识别样本的稀疏系数,根据稀疏系数计算并选取最小残差值对应的行为作为识别结果。实验结果表明,该方法识别移动用户行为的准确率可达82.64%,高于传统方法的识别准确率,且对随机投影压缩后的行为数据也具有良好的识别效果。

关键词: 行为识别,压缩感知,冗余字典

Abstract: To increase the recognition accuracy of mobile users’ behaviors,a compressed sensing based recognition method was proposed,which is able to recognize behaviors from raw acceleration data or compressed acceleration data.According to the theory that an over-comprehensive dictionary is able to reconstruct data,an over-comprehensive dictionary is constructed by raw acceleration data from three-axis accelerometer firstly,and then to calculate the sparse coefficient for samples to be tested by solving the minimum l1 norm.At last,residual values are calculated according to the behaviors and the minimum one is selected as the indicator to obtain the classification results.Experiment results show that by using this method the recognition accuracy can reach to 82.64%,which is higher than the recognition accuracy by using traditional recognition algorithms.At the same time,the recognition accuracy for the compressed acceleration data is also satisfied.

Key words: Behavior recognition,Compressed sensing,Over-comprehensive dictionary

[1] SHOAIB M,BOSCH S,INCEL O D, et al.A Survey of Online Activity Recognition Using Mobile Phones[J].Sensors,2015,15(1):2059-2085.
[2] HOSEINI-TABATABAEI S A,GLUHAK A,TAFAZOLLI R.A survey on smart phone-based systems for opportunistic user context recognition[J].ACM Computing Surveys (CSUR),2013,45(3):1-51.
[3] SHOAIB M,BOSCH S,DURMAZLNCEL O,et al.Fusion ofSmartphone Motion Sensors for Physical Activity Recognition[J].Sensors,2014,14(6):10146-10176.
[4] HOU C J,CHEN L,LV M Q,et al.Acceleration-based Activity Recognition Independent of Device Orientation and Placement[J].Computer Science,2014,41(10):76-79.(in Chinese) 侯仓健,陈岭,吕明琪,等.基于加速度传感器的放置方式和位置无关运动识别[J].计算机科学,2014,41(10):76-79.
[5] ZHANG M ,SAWCHUK A A.A Feature Selection-based Fra-mework for Human Activity Recognition Using Wearable Multimodal Sensors[C]∥International Conference on Body Area Networks.Beijing,2011.
[6] WANG Z M,CAO D.A Feature Selection Method for Behavior Recognition Based on Ant Colony Algorithm[J].Journal of Xi’an University of Posts and Telecommunications,2014,19(1):73-77.(in Chinese) 王忠民,曹栋.基于蚁群算法的行为识别特征优选方法[J].西安邮电大学学报,2014,19(1):73-77.
[7] ZHANG M,SAWCHUK A A.Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors[J].IEEE Journal of Biomedical and Health Informatics,2013,17(3):553-560.
[8] WU W Y,ZAHGN M,SAWCHUK A A,et al.Co-Recognition of Human Activity and Sensor Location via Compressed Sensing in Wearable Body Sensor Networks[C]∥Ninth International Conference on Wearable and Implantable Body Sensor Networks.London,2012.
[9] XIAO L,LI R F,LUO J.Recognition of Human Activity Based on Compressed Sensing in Body Sensor Networks[J].Journal of Electronics & Information Technology,2013,35(1):119-125.(in Chinese) 肖玲,李仁发,罗娟.体域网中一种基于压缩感知的人体行为识别方法[J].电子与信息学报,2013,35(1):119-125.
[10] SONG H,WANG Z M.Activity Recognition with Mobile Phone Accelerometers by Using Sparse Matrix Dictionary Method[J].Application Research of Computers,2015,32(9):2590-2592.(in Chinese) 宋辉,王忠民.基于稀疏矩阵字典的移动用户行为识别方法[J].计算机应用研究,2015,32(9):2590-2592.
[11] DONOBO D.Compressed Sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[12] BARANIUK R,CANDES E,ELAD M,et al.Applications ofSparse Representation and Compressive Sensing [Scanning the Issue][J].Proceedings of The IEEE,2010,98(6):906-909.
[13] CANDS E,TAO T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[14] CANDES E,ROMBERG J, TAO T.Robust Uncertainty Principles:Exact Signal Reconstruction from Highly Incomplete Frequency Information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[15] AKIMURA D,KAWAHARA Y,ASAMI T.Compressed Sen-sing Method for Human Activity Sensing using Mobile Phone Accelerometers[C]∥Ninth International Conference on Networked Sensing Systems.Belgium,2012.
[16] CANDES E,CALTECH J R.l1-Magic:Recovery of Sparse Signals via Convex Programming[EB/OL].(2005-10-1) [2009-01-29].http://statweb.stanford.edu/~candes/l1magic/ downloads/l1magic.pdf.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!