Recognition of Daily and Sports Activities

Inanc N., Kayri M., Ertugrul O. F.

IEEE International Conference on Big Data (Big Data), Washington, United States Of America, 10 - 13 December 2018, pp.2216-2220 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Washington
  • Country: United States Of America
  • Page Numbers: pp.2216-2220
  • Keywords: Daily and sports activity, action recognition, gender recognition, wearable sensor, MACHINE
  • Van Yüzüncü Yıl University Affiliated: No


Since being physically inactive was reported as one of the major risk factor of mortality, classifying daily and sports activities becomes a critical task that may improve human life quality. In this paper, the daily and sports activities dataset was used in order to evaluate and validate the employed approach. In this approach, the statistical features were extracted from the histograms of the local changes in the wearable sensors logs were obtained by one-dimensional local binary patterns. Later, extracted features were classified by extreme learning machines. Results were showed that the proposed approach is enough to recognize the action type, but in order to recognize the actions, or gender, different feature extraction methods must be employed.