İnanç, NihatKayri, MuratErtuğrul, Ömer Faruk2020-06-252020-06-252018closedAccess978-1-5386-5035-62639-1589https://hdl.handle.net/20.500.12587/7455IEEE International Conference on Big Data (Big Data) -- DEC 10-13, 2018 -- Seattle, WASince 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.eninfo:eu-repo/semantics/closedAccessDaily and sports activityaction recognitiongender recognitionwearable sensorConference Object221622202-s2.0-85062628607N/AWOS:000468499302038N/A