Skip to main content
SpringerLink
Log in

Accelerometer-based gesture recognition using dynamic time warping and sparse representation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a new accelerometer-based gesture recognition system. In this system, the start and end of the data collection process is automatically determined by acceleration waveform. In pretreatment phase, we propose a waveform compensation algorithm to solve the problems caused by the amplitude range of the accelerometer and use the coordinate transformation theory to alleviate the angle offset. In training phase, we use dynamic time warping (DTW) and affinity propagation (AP) to extract clusters and exemplars. We implement sparse representation for gesture recognition and propose a modified variable sparsity adaptive matching pursuit (MVSAMP) algorithm for signal reconstruction. This algorithm is more adapted to the characteristics of gesture recognition. In the classification stage, a method of weighted residuals is applied to improve the resolution of the best classification. To test the system’s performance, a dictionary of 10 gestures is defined and a database consists of 3800 samples is created from 14 participants. Test results have shown that the proposed system achieves a good performance in a variety of experiments on Android platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Akl A, Feng C, Valaee S (2011) A novel accelerometer-based gesture recognition system[J]. Signal Proc IEEE Trans 59(12):6197–6205

    Article  MathSciNet  Google Scholar 

  2. Akl A, Valaee S (2010) Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing[C]//Acoustics Speech and Signal Processing (ICASSP), 2010 I.E. International Conference on. IEEE, 2270–2273

  3. Boyali A, Kavakli M. A Robust gesture recognition algorithm based on Sparse Representation, random projections and Compressed Sensing// Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on

  4. Candès EJ, Wakin MB (2008) An introduction to compressive sampling[J]. Signal Proc Mag IEEE 25(2):21–30

    Article  Google Scholar 

  5. Concha O P, Xu RYD, Piccardi M (2010) Compressive sensing of time series for human action recognition[C]//Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on. IEEE, 454–461

  6. Do TT, Gan L, Nguyen N et al. (2008) Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//Signals, Systems and Computers, 2008 42nd Asilomar Conference on. IEEE, 581–587

  7. Frey BJ, Dueck D (2007) Clustering by passing messages between data points[J]. Science 315(5814):972–976

    Article  MathSciNet  MATH  Google Scholar 

  8. Hartmann B, Link N (2010) Gesture recognition with inertial sensors and optimized DTW prototypes[C]//Systems Man and Cybernetics (SMC), 2010 I.E. International Conference on. IEEE, 2102–2109

  9. Holzinger A, Softic S, Stickel C, et al. (2009) Intuitive e-teaching by using combined hci devices: experiences with wiimote applications[M]. Springer Berlin Heidelberg

  10. Holzinger A, Softic S, Stickel C et al (2012) Nintendo wii remote controller in higher education: development and evaluation of a demonstrator kit for e-teaching[J]. Comput Inform 29(4):601–615

    Google Scholar 

  11. Keogh E (2002) Exact indexing of dynamic time warping[C]//Proceedings of the 28th international conference on Very Large Data Bases. VLDB Endowment, 406–417

  12. Kettebekov S, Sharma R (2000) Understanding gestures in multimodal human computer interaction[J]. Int J Artif Intell Tools 9(02):205–223

    Article  Google Scholar 

  13. Mantyla V M, Mantyjarvi J, Seppanen T, et al. Hand gesture recognition of a mobile device user[C]//Multimedia and Expo, 2000. ICME 2000. 2000 I.E. International Conference on. IEEE, 2000, 1: 281–284

  14. Moni MA, Ali ABMS (2009) HMM based hand gesture recognition: A review on techniques and approaches[C]//Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on. IEEE, 433–437

  15. Montoliu R, Gatica-Perez D (2010) Discovering human places of interest from multimodal mobile phone data[C]//Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia. ACM: 12

  16. Neverova N, Wolf C, Paci G et al. (2013) A multi-scale approach to gesture detection and recognition[C]//Computer Vision Workshops (ICCVW), 2013 I.E. International Conference on. IEEE, 484–491

  17. Neverova N, Wolf C, Taylor GW et al. (2014) Multi-scale deep learning for gesture detection and localization[C]//Computer Vision-ECCV 2014 Workshops. Springer International Publishing, 474–490

  18. Neverova N, Wolf C, Taylor GW et al. (2014) ModDrop: adaptive multi-modal gesture recognition[J]. arXiv preprint arXiv:1501.00102

  19. Pavlovic VI, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human-computer interaction: A review[J]. Pattern Anal Mach Intell IEEE Trans 19(7):677–695

    Article  Google Scholar 

  20. Pylvänäinen T (2005) Accelerometer based gesture recognition using continuous HMMs[M]//Pattern Recognition and Image Analysis. Springer Berlin Heidelberg 639–646

  21. Ren Z, Meng J, Yuan J, et al. (2011) Robust hand gesture recognition with kinect sensor[C]//Proceedings of the 19th ACM international conference on Multimedia. ACM, 759–760

  22. Shen X, Hua G, Williams L et al (2012) Dynamic hand gesture recognition: an exemplar-based approach from motion divergence fields[J]. Image Vis Comput 30(3):227–235

    Article  Google Scholar 

  23. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit[J]. Inform Theory IEEE Trans 53(12):4655–4666

    Article  MathSciNet  MATH  Google Scholar 

  24. Wang X, Dai G (2007) A Novel Method to Recognize Complex Dynamic Gesture by Combining HMM and FNN Models[C]//Computational Intelligence in Image and Signal Processing, 2007. CIISP 2007. IEEE Symposium on. IEEE, 13–18

  25. Wright J, Ma Y, Mairal J et al (2010) Sparse representation for computer vision and pattern recognition[J]. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  26. Wright J, Yang AY, Ganesh A et al (2009) Robust face recognition via sparse representation[J]. Pattern Anal Mach Intell IEEE Trans 31(2):210–227

    Article  Google Scholar 

  27. Yao A, Gool LV, Kohli P (2014) Gesture recognition portfolios for personalization[C]//Computer Vision and Pattern Recognition (CVPR), 2014 I.E. Conference on. IEEE, 1923–1930

  28. Zhang Y (2008) Theory of Compressive Sensing via ℓ 1-Minimization: a Non-RIP Analysis and Extensions[J]. Journal of the Operations Research Society of China, 1–27

Download references

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Network System and Network Culture, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Haiying Wang & Zhengshan Li

Authors
  1. Haiying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengshan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengshan Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Li, Z. Accelerometer-based gesture recognition using dynamic time warping and sparse representation. Multimed Tools Appl 75, 8637–8655 (2016). https://doi.org/10.1007/s11042-015-2775-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2775-2

Keywords

Search

Navigation