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Multimedia Tools and Applications

, Volume 77, Issue 16, pp 21201–21220 | Cite as

Segmentation and recognition of human motion sequences using wearable inertial sensors

  • Ming Guo
  • Zhelong Wang
Article

Abstract

The application of human motion monitoring technology based on wearable inertial sensors has achieved great success in the last ten years. But now the research is mainly focused on isolated motion recognition, and there is scarce research on recognition of human motion sequences. In this paper a novel monitoring framework of human motion sequences is proposed based on wearable inertial sensors. The monitoring framework is composed of data acquisition, segmentation, and recognition stages; the main work of this paper is the last two parts. At the segmentation stage, SVD is used to perform pre-segmentation of motion sequence and its purpose is to reduce time in the segmentation process as much as possible. Then a novel similarity measure named MSHsim is proposed to accomplish the fine segmentation. At the recognition stage an HMM is used to recognize the motion sequence. We use four inertial sensors to collect the human motion data. Experiments are implemented to evaluate the performance of the proposed monitoring framework, and from the experiment results, it can be seen that the proposed method may achieve better performance compared to other methods.

Keywords

Wearable inertial sensors Human motion sequence Pre-segmentation Fine segmentation Motion recognition 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant No.61473058, Fundamental Research Funds for the Central Universities (DUT15ZD114) and Project Funded by China Postdoctoral Science Foundation (2017M621131). The authors gratefully acknowledge the assistance of Mark V. Albert in correcting English language.

References

  1. 1.
    Amft O (2011) Self-taught learning for activity spotting in on-body motion sensor data. In: 2011 15th annual international symposium on wearable computers. IEEE, pp 83–86Google Scholar
  2. 2.
    Amft O, Bannach D, Pirkl G, Kreil M, Lukowicz P (2010, April) Towards wearable sensing-based assessment of fluid intake. In: PerCom Workshops, pp 298–303Google Scholar
  3. 3.
    Amft O, Troster G (2008) Recognition of dietary activity events using on-body sensors. Artif Intell Med 42(2):121–136CrossRefGoogle Scholar
  4. 4.
    Andreu J, Angelov P (2010) Real-time human activity recognition from wireless sensors using evolving fuzzy systems. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, pp 1–8Google Scholar
  5. 5.
    Bama SS, Ahmed ML, Saravanan A (2015) A survey on performance evaluation measures for information retrieval systemGoogle Scholar
  6. 6.
    Bhimani J, Mi N, Leeser M, Yang Z (2017) FIM: performance prediction for parallel computation in iterative data processing applications. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, pp 359–366Google Scholar
  7. 7.
    Blanke U, Schiele B (2009) Daily routine recognition through activity spotting. In: International symposium on location-and context-awareness. Springer, Berlin, pp 192–206Google Scholar
  8. 8.
    Candes E, Li X, Ma Y, Wright J (2010) Robust principal component analysis?: recovering low-rank matrices from sparse errors. 8(1):201–204Google Scholar
  9. 9.
    Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808CrossRefGoogle Scholar
  10. 10.
    Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781CrossRefGoogle Scholar
  11. 11.
    Ghaleb FF, Youness EA, Elmezain M, Dewdar FS (2015) Vision-based hand gesture spotting and recognition using CRF and SVM. J Softw Eng Appl 8(07):313CrossRefGoogle Scholar
  12. 12.
    Ghassemzadeh H, Guenterberg E, Ostadabbas S, Jafari R (2009) A motion sequence fusion technique based on pca for activity analysis in body sensor networks. In: 2009 annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 3146–3149Google Scholar
  13. 13.
    Guan D, Ma T, Yuan W, Lee YK, Jehad Sarkar AM (2011) Review of sensor-based activity recognition systems. IETE Tech Rev 28(5):418–433CrossRefGoogle Scholar
  14. 14.
    Guo J, Xie X, Bie R, Sun L (2014) Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks. Pers Ubiquit Comput 18(8):1977–1987CrossRefGoogle Scholar
  15. 15.
    Guo M, Wang Z (2015) A feature extraction method for human action recognition using body-worn inertial sensors. In: 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, pp 576–581Google Scholar
  16. 16.
    Hammerla NY, Halloran S, Ploetz T (2016) Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables. arXiv:1604.08880
  17. 17.
    Junker H, Amft O, Lukowicz P, Trrster G (2008) Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recogn 41(6):2010–2024CrossRefMATHGoogle Scholar
  18. 18.
    Kunze K, Barry M, Heinz EA, Lukowicz P, Majoe D, Gutknecht J (2006) Towards recognizing tai chi-an initial experiment using wearable sensors. In: 2006 3rd International Forum on Applied Wearable Computing (IFAWC). VDE, pp 1–6Google Scholar
  19. 19.
    Ladha C, Hammerla NY, Olivier P, Plotz T (2013) ClimbAX: skill assessment for climbing enthusiasts. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, pp 235–244Google Scholar
  20. 20.
    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15(3):1192–1209CrossRefGoogle Scholar
  21. 21.
    Lee MW, Khan AM, Kim TS (2011) A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation. Pers Ubiquit Comput 15(8):887–898CrossRefGoogle Scholar
  22. 22.
    Leutheuser H, Schuldhaus D, Eskofier BM (2013) Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PloS One 8(10):e75196CrossRefGoogle Scholar
  23. 23.
    Li C, Zheng SQ, Prabhakaran B (2007) Segmentation and recognition of motion streams by similarity search. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 3(3):16CrossRefGoogle Scholar
  24. 24.
    Li K, Fu Y (2014) Prediction of human activity by discovering temporal sequence patterns. IEEE Trans Pattern Anal Mach Intell 36(8):1644–1657CrossRefGoogle Scholar
  25. 25.
    Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184CrossRefGoogle Scholar
  26. 26.
    Lu Y, Wei Y, Liu L (2016) Towards unsupervised physical activity recognition using smartphone accelerometers[J]. Multimed Tool Appl 2016:1–19Google Scholar
  27. 27.
    Murtaza M, Sharif M, Raza M, Shah J (2014) Face recognition using adaptive margin fishers criterion and linear discriminant analysis. Int Arab J Inform Technol 11 (2):1–11Google Scholar
  28. 28.
    Ni B, Wang G, Moulin P (2013) Rgbd-hudaact: a color-depth video database for human daily activity recognition. In: Consumer Depth Cameras for Computer Vision. Springer, London, pp 193–208Google Scholar
  29. 29.
    Ofli F, Chaudhry R, Kurillo G, Vidal R, Bajcsy R (2014) Sequence of the most informative joints (smij): a new representation for human skeletal action recognition. J Vis Commun Image Represent 25(1):24–38CrossRefGoogle Scholar
  30. 30.
    Ogris G, Stiefmeier T, Lukowicz P, Troster G (2008) Using a complex multi-modal on-body sensor system for activity spotting . In: 2008 12th IEEE international symposium on wearable computers. IEEE, pp 55–62Google Scholar
  31. 31.
    Ordonez FJ, de Toledo P, Sanchis A (2015) Sensor-based bayesian detection of anomalous living patterns in a home setting. Pers Ubiquit Comput 19(2):259–270CrossRefGoogle Scholar
  32. 32.
    Paradiso R, Loriga G, Taccini N (2005) A wearable health care system based on knitted integrated sensors. IEEE Trans Inf Technol Biomed 9(3):337–344CrossRefGoogle Scholar
  33. 33.
    Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, No. 22. IBM, New York, pp 41–46Google Scholar
  34. 34.
    Shi Q, Cheng L, Wang L, Smola A (2011) Human action segmentation and recognition using discriminative semi-markov models. Int J Comput Vis 93(1):22–32CrossRefMATHGoogle Scholar
  35. 35.
    Singla G, Cook DJ, Schmitter-Edgecombe M (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Humaniz Comput 1(1):57–63CrossRefGoogle Scholar
  36. 36.
    Song Y, Huang J, Zhou D, Zha H, Giles CL (2007) Iknn: informative k-nearest neighbor pattern classification. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin, pp 248–264Google Scholar
  37. 37.
    Stewart GW (1973) Error and perturbation bounds for subspaces associated with certain eigenvalue problems. SIAM Rev 15(4):727–764MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Van Kasteren TLM, Englebienne G, Krose BJ (2010) An activity monitoring system for elderly care using generative and discriminative models. Pers Ubiquit Comput 14(6):489–498CrossRefGoogle Scholar
  39. 39.
    Wang L, Wang X, Leckie C, Ramamohanarao K (2008) Characteristic-based descriptors for motion sequence recognition. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 369–380Google Scholar
  40. 40.
    Wang Z, Guo M, Zhao C (2016) Badminton stroke recognition based on body sensor networks. IEEE Trans Human-Machine Syst 46(5):769–775CrossRefGoogle Scholar
  41. 41.
    Wu D, Wang Z, Chen Y, Zhao H (2016) Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 190:35–49CrossRefGoogle Scholar
  42. 42.
    Yang F, Zhu Y, Shi BL (2004) An efficient method for similarity search on quantitative transaction data. J Comput Res Develop 41(2):361–368Google Scholar
  43. 43.
    Zhao X, Li X, Pang C, Zhu X, Sheng QZ (2013) Online human gesture recognition from motion data streams. In: Proceedings of the 21st ACM international conference on multimedia. ACM, pp 23–32Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Control Science and EngineeringDalian University of TechnologyDalianChina

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