Research on fall detection based on Adaboost multiple kernel support vector machine

Abstract

Keywords:
• AdaBoost,
• multi-kernel learning,
• SVM,
• fall detection

References

1. [1]

   MUBASHIR M,SHAO L,SEED L.A survey on fall detection:principles and approaches[J].Neurocomputing,2013,100:144.

2. [2]

   王之琼,曲璐渲,隋雨彤,等.基于极限学习机的跌倒检测分类识别研究[J].中国生物医学工程学报,2014,31(4):418.
   

3. [3]

   ZIGEL Y,LITVAK D,GANNOT I.A method for automatic fall detection of elderly people using floor vibrations and sound-Proof of concept on human mimicking doll falls[J].IEEE Transactions on Biomedical Engineering,2009,56(12):2858.

4. [4]

   FOROUGHI H,ASKI B S,POURREZA H.Intelligent video surveillance for monitoring fall detection of elderly in home environments[C]//200811th Int Conf on Computer and Information Technology.Piscataway:IEEE,2008:219.

5. [5]

   LEE J K,ROBINOVITCH S N,PARK E J.Inertial sensing-based pre-impact detection of falls involving near-fall scenarios[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2015,23(2):258.

6. [6]

   LAI C F,CHANG S Y,CHAO H C,et al.Detection of cognitive injured body region using multiple triaxial accelerometers for elderly falling[J].IEEE Sensors Journal,2011,11(3):763.

7. [7]

   AZIZ O,ROBINOVITCH S N.An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans[J].IEEE Transactions on neural Systems and Rehabilitation Engineering,2011,19(6):670.

8. [8]

   CHENG W C,JHAN D M.Triaxial accelerometer-based fall detection method using a self-constructing cascade-AdaBoost-SVM classifier[J].IEEE Journal of Biomedical and Health Iinformatics,2013,17(2):411.

9. [9]

   SHANY T,REDMOND S J,NARAYANAN M R,et al.Sensors-based wearable systems for monitoring of human movement and falls[J].IEEE Sensors Journal,2012,12(3):658.

10. [10]

    KANGAS M,VIKMAN I,WIKLANDER J,et al.Sensitivity and specificity of fall detection in people aged 40 years and over[J].Gait & posture,2009,29(4):571.

11. [11]

    TONG L,SONG Q,GE Y,et al.HMM-based human fall detection and prediction method using tri-axial accelerometer[J].IEEE Sensors Journal,2013,13(5):1849.

12. [12]

    KANGAS M,KONTTILA A,WINBLAD I,et al.Determination of simple thresholds for accelerometry-based parameters for fall detection[C]//200729th Annual International Conference of the IEEE.Engineering in Medicine and Biology Society,Piscataway:IEEE,2007:1367.

13. [13]

    GOH A T C.Back-propagation neural networks for modeling complex systems[J].Artificial Intelligence in Engineering,1995,9(3):143.

14. [14]

    陈强,任雪梅.基于多核最小二乘支持向量机的永磁同步电机混沌建模及其实时在线预测[J].物理学报,2010,59(4):2310.
    

15. [15]

    ZHANG Y,FU P,LIU W,et al.SVM classification for imbalanced data using conformal kernel transformation[C]//2014 International Joint Conference on Neural Networks (IJCNN).Piscataway:IEEE,2014:2894.

16. [16]

    张凯军,梁循.一种改进的显性多核支持向量机[J].自动化学报,2014,40(10):2288.
    

17. [17]

    亓晓振,王庆.一种基于稀疏编码的多核学习图像分类方法[J].电子学报,2012,40(4):773.
    

18. [18]

    王新迎,韩敏.多元混沌时间序列的多核极端学习机建模预测[J].物理学报,2015,64(7):70504.
    

19. [19]

    SUYKENS J A K,VANDEWALLE J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293.

20. [20]

    YANG X,TAN L,HE L.A robust least squares support vector machine for regression and classification with noise[J].Neurocomputing,2014,140:41.

21. [21]

    SCHAPIRE R E.The boosting approach to machine learning:an overview[M]//Nonlinear estimation and classification.New York:Springer,2003:149.

22. [22]

    WANG Z,JIANG M,HU Y,et al.An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors[J].IEEE Transactions on Information Technology in Biomedicine,2012,16(4):691.

23. [23]

    ALTUN K,BARSHAN B,TUNGEL O.Comparative study on classifying human activities with miniature inertial and magnetic sensors[J].Pattern Recognition,2010,43(10):3605.

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