Journal of Medical and Biological Engineering

, Volume 38, Issue 4, pp 544–555 | Cite as

Aerobic Exercise Recognition Through Sparse Representation Over Learned Dictionary by Using Wearable Inertial Sensors

  • Ming Guo
  • Zhelong WangEmail author
  • Ning Yang
Original Article


Aerobic exercise is conducive to reducing the risks of cardiovascular disease and central arterial stiffness. However, it can also cause some health hazards (such as tissue oxidative damage), especially for the elderly. It is essential to recognize and monitor different aerobic exercises for the health of exercisers. In this paper, a multi-sensor monitoring system is established for aerobic exercise recognition, and a novel recognition algorithm based on dictionary learning algorithm and sparse representation is proposed. Eight volunteers are invited to carry out ten activities, and five wireless inertial sensor nodes are used to collect the sensor data. Several experiments are implemented to verify the effectiveness of the recognition algorithm proposed in the paper. According to the experimental results, our method achieves the best performance than four other recognition algorithms including decision tree C4.5, naive Bayes, support vector machine and sparse representation. Besides, the other two aspects are also studied in the paper, one is the effect of different binding positions of sensors on classification results, and the other is the effect of selecting different features. The results of the experiments show that two sensor nodes attached to the right wrist and the left thigh achieve better result, and the feature “correlation coefficient” is not important to recognize different aerobic exercises that are investigated in our paper.


Aerobic exercise recognition Wearable inertial sensors Sparse representation Learned dictionary Intelligent health care 



This work was supported by National Natural Science Foundation of China under Grant No.61473058, Fundamental Research Funds for the Central Universities (DUT 15ZD114).


  1. 1.
    Whelton, S. P., Chin, A., Xin, X., & He, J. (2002). Effect of aerobic exercise on blood pressure: A meta-analysis of randomized, controlled trials. Annals of Internal Medicine, 136(7), 493–503.CrossRefGoogle Scholar
  2. 2.
    Berlin, J. A., & Colditz, G. A. (1990). A meta-analysis of physical activity in the prevention of coronary heart disease. American Journal of Epidemiology, 132(4), 612–628.CrossRefGoogle Scholar
  3. 3.
    Hayashi, K., Sugawara, J., Komine, H., Maeda, S., & Yokoi, T. (2005). Effects of aerobic exercise training on the stiffness of central and peripheral arteries in middle-aged sedentary men. The Japanese Journal of Physiology, 55(4), 235–239.CrossRefGoogle Scholar
  4. 4.
    Colcombe, S. J., Erickson, K. I., & Scalf, P. E. (2006). Aerobic exercise training increases brain volume in aging humans. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(11), 1166–1170.CrossRefGoogle Scholar
  5. 5.
    Packer, L. (1997). Oxidants, antioxidant nutrients and the athlete. Journal of Sports Sciences, 15(3), 353–363.CrossRefGoogle Scholar
  6. 6.
    Jenkins, R. R. (2000). Exercise and oxidative stress methodology: A critique. The American Journal of Clinical Nutrition, 72(2), 670–674.CrossRefGoogle Scholar
  7. 7.
    Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 790–808.CrossRefGoogle Scholar
  8. 8.
    Lara, O. D., & Labrador, M. A. (2013). A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials, 15(3), 1192–1209.CrossRefGoogle Scholar
  9. 9.
    Fortino, G., Galzarano, S., Gravina, R., & Li, W. (2015). A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Information Fusion, 22, 50–70.CrossRefGoogle Scholar
  10. 10.
    Pantelopoulos, A., & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(1), 1–12.CrossRefGoogle Scholar
  11. 11.
    Wu, H. H., Lemaire, E. D., & Baddour, N. (2012). Activity change-of-state identification using a blackberry smartphone. Journal of Medical and Biological Engineering, 32(4), 265–272.CrossRefGoogle Scholar
  12. 12.
    Kozina, S., Gjoreski, H., Gams, M., & Lustrek, M. (2013). Three-layer activity recognition combining domain knowledge and meta-classification. Journal of Medical and Biological Engineering, 33(4), 406–414.CrossRefGoogle Scholar
  13. 13.
    Guilln, S., Arredondo, M. T., & Castellano E. (2011). A survey of commercial wearable systems for sport application. In Wearable Monitoring Systems (pp. 165–178).Google Scholar
  14. 14.
    Wang, Z., Zhao, C., & Qiu, S. (2014). A system of human vital signs monitoring and activity recognition based on body sensor network. Sensor Review, 34(1), 42–50.CrossRefGoogle Scholar
  15. 15.
    Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.CrossRefGoogle Scholar
  16. 16.
    Mairal, J., Elad, M., & Sapiro, G. (2008). Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17(1), 53–69.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., & Ma, Y. (2012). Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 372–386.CrossRefGoogle Scholar
  18. 18.
    Yuan, X. T., Liu, X., & Yan, S. (2012). Visual classification with multitask joint sparse representation. IEEE Transactions on Image Processing, 21(10), 4349–4360.MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Guha, T., & Ward, R. K. (2012). Learning sparse representations for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(8), 1576–1588.CrossRefGoogle Scholar
  20. 20.
    Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19–60.MathSciNetzbMATHGoogle Scholar
  21. 21.
    Yang, A. Y., Jafari, R., Sastry, S. S., & Bajcsy, R. (2009). Distributed recognition of human actions using wearable motion sensor networks. Journal of Ambient Intelligence and Smart Environments, 1(2), 103–115.Google Scholar
  22. 22.
    Xu, W., Zhang, M., Sawchuk, A. A., & Sarrafzadeh, M. (2012). Robust human activity and sensor location corecognition via sparse signal representation. IEEE Transactions on Biomedical Engineering, 59(11), 3169–3176.CrossRefGoogle Scholar
  23. 23.
    Zhang, M., & Sawchuk, A. A. (2013). Human daily activity recognition with sparse representation using wearable sensors. IEEE Journal of Biomedical and Health Informatics, 17(3), 553–560.CrossRefGoogle Scholar
  24. 24.
    Akimura, D., Kawahara, Y., & Asami, T. (2012). Compressed sensing method for human activity sensing using mobile phone accelerometers. In Networked Sensing Systems (INSS), 2012 Ninth International Conference on (pp. 1–4). IEEE.Google Scholar
  25. 25.
    Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.CrossRefzbMATHGoogle Scholar
  26. 26.
    Ermes, M., Pärkkä, J., Mäntyjärvi, J., & Korhonen, I. (2008). Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine, 12(1), 20–26.CrossRefGoogle Scholar
  27. 27.
    Perez-Lopez, C., Català, A., Rodriguez-Martin, D., Samà, A., Perez-Lopez, C., Català, A., et al. (2013). SVM-based posture identification with a single waist-located triaxial accelerometer. Expert Systems with Applications, 40(18), 7203–7211.CrossRefGoogle Scholar
  28. 28.
    Khan, A. M., Lee, Y. K., Lee, S. Y., & Kim, T. S. (2010). A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEETransactions on Information Technology in Biomedicine, 14(5), 1166–1172.CrossRefGoogle Scholar
  29. 29.
    Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., & Valenzuela, O. (2013). Human activity recognition based on a sensor weighting hierarchical classifier. Soft Computing, 17(2), 333–343.CrossRefGoogle Scholar
  30. 30.
    Atallah, L., Lo, B., King, R., & Yang, G. Z. (2010). Sensor placement for activity detection using wearable accelerometers. In International Conference on Body Sensor Networks IEEE (pp. 24–29).Google Scholar
  31. 31.
    Kai, K., & Lukowicz, P. (2014). Sensor placement variations in wearable activity recognition. IEEE Pervasive Computing, 13(4), 32–41.CrossRefGoogle Scholar
  32. 32.
    Ravi, N., Dandekar, N., Mysore, P., & Littman, M. L. (2005). Activity recognition from accelerometer. AAAI, 5, 1541–1546.Google Scholar
  33. 33.
    Bao, L., & Intille S. S. (2004). Activity recognition from user-annotated acceleration data. In International Conference on Pervasive Computing (pp. 1–17). Heidelberg: Springer.Google Scholar
  34. 34.
    Guo, M., & Wang, Z. (2015). A feature extraction method for human action recognition using body-worn inertial sensors. In IEEE, International Conference on Computer Supported Cooperative Work in Design IEEE (pp. 576–581).Google Scholar
  35. 35.
    El-Bakry, A. S., Tapia, R. A., Tsuchiya, T., & Zhang, Y. (1996). On the formulation and theory of the Newton interior-point method for nonlinear programming. Journal of Optimization Theory and Applications, 89(3), 507–541.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

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

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