Baris Yamansavascilar

Software Engineer

Activity Recognition on Smartphones

Great hardware and software capabilities of mobile devices allow us to research new scientific fields. Activity recognition is one of the main research areas for smartphones. Built-in sensors of a standard smartphone, such as accelerometer, magnetometer, gyroscope, enable us to predict the daily activities of a person. However, continuous samplings from each sensor increase the CPU utilization and cause the battery to die sooner. In this study, we focused on the window sizes and the sampling rates in order to observe how they affect to the accuracy and CPU utilization.

Related Publications:


Activity Recognition on Smartphones: Efficient Sampling Rates and Window Sizes , Baris Yamansavascilar and M. Amac Guvensan, The International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications (PerMoby), pp. 1-6, 2016