eHealth 360° pp 306-314 | Cite as

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 181)


Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance.


Inertial measurement unit Accelerometer data Feature extraction Data-fusion Machine learning algorithms Human Activity Recognition 



Tahmina Zebin would like to thank the Presidents Doctoral Scholar award scheme, University of Manchester for funding her PhD.


  1. 1.
    Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 1–33 (2014)CrossRefGoogle Scholar
  2. 2.
    Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., Fuks, H.: Wearable computing: accelerometers’ data classification of body postures and movements. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS, vol. 7589, pp. 52–61. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)CrossRefGoogle Scholar
  4. 4.
    Shaopeng, L., Gao, R.X., John, D., Staudenmayer, J.W., Freedson, P.S.: Multisensor data fusion for physical activity assessment. IEEE Trans. Biomed. Eng. 59(3), 687–696 (2012)CrossRefGoogle Scholar
  5. 5.
    Bettini, C., et al.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
  6. 6.
    Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013)CrossRefGoogle Scholar
  7. 7.
    Ronao, C.A., Sung-Bae, C.: Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. In: 10th International Conference on Natural Computation (ICNC-2014), pp. 681–686 (2014)Google Scholar
  8. 8.
    Turaga, P., et al.: Machine recognition of human activities: a survey. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1473–1488 (2008)CrossRefGoogle Scholar
  9. 9.
    Fourati, H. (ed.): Multisensor Data Fusion: From Algorithms and Architectural Design to Applications, pp. 509–517. CRC Press, Taylor & Francis Group LLC, United States (2015)Google Scholar
  10. 10.
    Bao, L., Intille, Stephen, S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24646-6_1 CrossRefGoogle Scholar
  11. 11.
    Zeng, M., et al.: Convolutional neural networks for human activity recognition using mobile sensors. In: 6th International Conference on Mobile Computing, Applications and Services (MobiCASE) 2014, pp. 197–205 (2014)Google Scholar
  12. 12.
    Kaufman, K.R.: Future directions in gait analysis. In: RRDS Gait Analysis in the Science of Rehabilitation, pp. 85–112 (2011)Google Scholar
  13. 13.
    Classification learner app for supervised machine learning.

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUK
  2. 2.Photon Science InstituteUniversity of ManchesterManchesterUK

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