№2, 2019

FEATURES SELECTION FOR HUMAN ACTIVITY RECOGNITION IN THE TELEREHABILITATION SYSTEM

Yelena S. Tarantova, Kirill V. Makarov, Alexey A. Orlov

The article researches the problem of human activity recognition in the telerehabilitation system. To recognize the patient's physical activity, the sensors of the smartphone are used: accelerometer and gyroscope. The term of telerehabilitation and an exemplary set of exercises as part of a rehabilitation event are considered. Such tasks as determining the permissible value of frequency and classification accuracy and also features select to reduce computational complexity are solved. The classification frequency value is proposed to take into account the type of activity and the patient's health group for evaluating the correctness of independent implementation of rehabilitation events by the patient. An algorithm for selecting a subset of informative features is described. An experiment is carried out to select a subset of informative features is necessary for the classification of physical activity in the telerehabilitation system, taking into account the influence of features on the classification accuracy and computational complexity in their calculation. Comparison of classification results using a feature vector and using a subset of informative features is performed (pp.49-59).

Keywords: feature extraction, feature selection, feature engineering, accelerometer, gyroscope, telerehabilitation, human activity recognition.
References
  • Vladzimirskij A.V., Lebedev G.S. Telemedicina, M.: GJeOTAR, Media, 2018, 576 s.
  • Lara O.D., Labrador M.A. A Survey on Human Activity Recognition using Wearable Sensors // IEEE Commun. Surv. Tutorials, 2013, vol.15, no.3, pp.1192–1209.
  • Walse K., Dharaskar R.V. A Survey on Human Activity Recognition using Smartphone // Int. J. Adv. Res. Comput. Sci. Manag. Stud, 2017, vol.5, no.3, pp.118–125.
  • Capela N.A., Lemaire E.D., Baddour N. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients // PLoS One. Public Library of Science, 2015, vol.10, no.4.
  • Hasan S.S. et al. Human Activity Recognition using Smartphone Sensors with Context Filtering / ACHI 2016 Ninth Int. Conf. Adv. Comput. Interact. Hum., 2016, vol.571–572, pp. 1019–1029.
  • Attila Reiss (2014). Personalized mobile physical activity monitoring for everyday life. (Doctoral Thesis, Technical University of Kaiserslautern). https://kluedo.ub.uni-kl.de/files/ 3681/ _PhDThesis_AttilaReiss.pdf
  • Jorge Luis Reyes Ortiz (2015).Smartphone-based human activity recognition (Doctoral Thesis, Universitat Politècnica de Catalunya). https://link.springer.com/book/10. 1007%2F978-3-319-14274-6.
  • He Y., Li Y. Physical activity recognition utilizing the built-in Kinematic sensors of a smartphone // Int. J. Distrib. Sens. Networks. SAGE PublicationsSage UK: London, England, 2013, vol.2013, no.4, pp.481–580.
  • Miao F. et al. Identifying typical physical activity on smartphone with varying positions and orientations // Biomed. Eng. Online. BioMed Central, 2015, vol.14, no.1, pp.32–46.
  • Bubnova M.G., Aronov D.M., Bojcov S.A. Obespechenie fizicheskoj aktivnosti u grazhdan, imejushhih ogranichenija v sostojanii zdorov'ja: Metodicheskie rekomendacii. Moskva: Federal'noe gosudarstvennoe bjudzhetnoe Uchrezhdenie «Gosudarstvennyj nauchno-issledovatel'skij centr profilakticheskoj mediciny», 2015, 95 s.
  • Shoaib M. et al. Fusion of smartphone motion sensors for physical activity recognition // Sensors (Switzerland), vol.14, pp.10146–10176, 2014.
  • Morillo L. et al. Low Energy Physical Activity Recognition System on Smartphones // Sensors, 2015, vol.15, no.3, pp.5163–5196.
  • Suryanarayana D. A Comparative Study of Random Forest & K – Nearest Neighbors on HAR dataset Using Caret // Int. J. Innov. Res. Technol, 2017, vol.3, no.9, pp.6–9.
  • Lara Ó.D. et al. Centinela: A human activity recognition system based on acceleration and vital sign data // Pervasive and Mobile Computing. Elsevier, 2012, vol.8, no.5. pp.717–729.
  • Bugdol M.D. et al. Human Activity Recognition Using Smartphone Sensors // International Research Journal of Engineering and Technology, 2016, pp.41–47.
  • Marinho L.B., de Souza Junior A.H., Rebouças Filho P.P. A New Approach to Human Activity Recognition Using Machine Learning Techniques / International conference on Intelligent Systems Design and Applications, Springer, Cham, 2017, pp.529–538.
  • Fan L., Wang Z., Wang H. Human Activity Recognition Model Based on Decision Tree / 2013 International Conference on Advanced Cloud and Big Data. IEEE, 2013, pp.64–68.
  • Zheng Y. Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework // J. Electr. Comput. Eng. Hindawi Publishing Corp., 2015, vol. 2015, pp.1–9.
  • Kononenko I. ReliefF for estimation and discretization of attributes in classification, regression, and ILP problems // Artif. Intell: methodology, systems, applications, 1996, pp.1–15.