Computer Science ›› 2022, Vol. 49 ›› Issue (5): 78-83.doi: 10.11896/jsjkx.210400024

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Human Activity Recognition Method Based on Class Increment SVM

XING Yun-bing1, LONG Guang-yu1,2, HU Chun-yu3, HU Li-sha4   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2 School of Computer Science & School of Cyberspace Science,Xiangtan University,Xiangtan,Hunan 411105,China
    3 Schoolof Computer Scienceand Technology,Qilu University of Technology,Jinan 250353,China
    4 School of Information Technology,Hebei University of Economics and Business,Shijiazhuang 050061,China
  • Received:2021-04-01 Revised:2021-10-18 Online:2022-05-15 Published:2022-05-06
  • About author:XING Yun-bing,born in 1982,master,senior engineer.His main research interests include sign language interaction,pervasive computing and health surveillance.
  • Supported by:
    National Key Research and Development Program of China(2018YFC2002603),National Natural Science Foundation of China(62002187) and Science and Technology Project of Hebei Education Department(QN2018116).

Abstract: Health monitoring based on human activity recognition (HAR) is an important means to discover health abnormalities.However,in daily activity recognition,it is difficult to obtain training samples containing all possible activity categories in advance.When new categories appear in the prediction stage,the traditional support vector machine (SVM) will incorrectly classify them as known category.A robust classifier should be able to distinguish the newly added categories so that they can be processed differently from the known categories.This paper proposes a human activity recognition method based on class increment SVM,and the idea of hypersphere is introduced,which can not only identify known activity categories with high accuracy,but also detect new categories.The multiple hyperspheres obtained through training divide the entire feature space,so that the classifier has the ability to detect newly added activity categories.The experimental results show that compared with the traditional multi-class SVM method,our method can realize the detection of new categories without significantly reducing the classification effect of known categories,thereby improving the classifier's ability to recognize human activity in an open environment.

Key words: Class increment, Clustering separability, Human activity recognition, Hyperspheres, Support vector machine

CLC Number: 

  • TP391.41
[1]LARA O D,LABABRADOR M A.A Survey on Human Acti-vity Recognition Using Wearable Sensors[J].IEEE Communications Surveys & Tutorials,2012,15(3):1192-1209.
[2]ZHANG C X,ZHAO C L,CHEN C,et al.Review of Human Activity Recognition Based on Mobile Phone Sensors[J].Computer Science,2020,47(10):1-8.
[3]DAVIDE A,ALESSANDRO G,LUCA O,et al.A Public Do-main Dataset for Human Activity Recognition Using Smartphones[C]//21th European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning(ESANN 2013).Bruges,Belgium,2013:24-26.
[4]WANG Z M,WANG K,HE Y.Human Motion Activity Recognition Model Based on Multi-classifier Fusion[J].Computer Science,2016,43(12):297-301.
[5]YANG X,CHEN Y,YU H,et al.Instance-Wise Dynamic Sensor Selection for Human Activity Recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:1104-1111.
[6]YANG W B,YANG H C,LU C,et al.Gesture RecognitionBased on Skin Color Features and Convolutional Neural Network[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2018,35(4):75-81.
[7]SONG X R,ZHANG X Q,ZHAN Z,et al.Multi-Sensor DataFusion for Complex Human Activity Recognition[J].Journal of Tsinghua University(Science and Technology),2020,60(10):814-821.
[8]DONG L H,LIU Q,CHEN H M,et al.A Lightweight Real-Time Motion Recognition Algorithm Based on Time Window[J].Journal of Computer Research and Development,2017,54(12):2731-2740.
[9]SHAAFI A,SALEM O,MEHAOUA A.Improving Human Activity Recognition Algorithms Using Wireless Body Sensors and SVM[C]//2020 International Wireless Communications and Mobile Computing (IWCMC).IEEE,2020.
[10]YANG J B,NGUYEN M N,SAN P P,et al.Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition[C]//International Conference on Artificial Intelligence.2015:3995-4001.
[11]ROKNI S A,NOUROLLAHI M,GHASEMZADEH H.Perso-nalized Human Activity Recognition Using Convolutional Neural Networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018.
[12]DENG S Z,WANG B T,YANG C G et al.Convolutional Neural Networks for Human Activity Recognition Using Multi-location Wearable Sensors[J].Journal of Software,2019,30(3):718-737.
[13]LI B,CUI W,WANG W,et al.Two-Stream Convolution Augmented Transformer for Human Activity Recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021.
[14]GENG C,HUANG S J,CHEN S.Recent Advances in Open Set Recognition:A Survey[J].arXiv:1811.08581,2020.
[15]SCHEIRER W J,JAIN L P,BOULT T E.Probability Models for Open Set Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(11):2317-2324.
[16]BUSTO P P,GALL J.Open Set Domain Adaptation[C]//IEEE International Conference on Computer Vision.IEEE Computer Society,2017:754-763.
[17]VARETOR,SILVA S,COSTA F,et al.Towards Open-Set Face Recognition Using Hashing Functions[C]//2018 IEEE International Joint Conference on Biometrics (IJCB).IEEE,2018.
[18]ANGUITA D,GHIO A,ONETO L,et al.A Public Domain Dataset for Human Activity Recognition Using Smartphones[C]//Proceedings of the 21th European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning(ESANN).2013:437-442.
[19]DUA D,GRAFF C.UCI Machine Learning Repository[EB/OL].
[20]JIANG X,CHEN Y,LIU J,et al.AIR:Recognizing Activity Through IR-Based Distance Sensing on Feet[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:Adjunct.ACM,2016:97-100.
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