Activity Recognition on Smartphones: Efficient Sampling Rates and Window Sizes

Abstract

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 recognize the daily activities of a person. 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. For our test scenarios, we built a dataset including a tri-axial accelerometer sensor data of 7 daily activities: walking, jogging, sitting, lying, standing, walking upstairs and walking downstairs. We collected these activities with a sampling rate of 80 Hz by using 5 seconds window size. Then, we downsampled the collected data to 40 Hz, 20 Hz, 10 Hz, 5 Hz and 1 Hz by using 4, 3, 2, and 1 seconds window sizes, respectively. Thus, we could evaluate the variation of the accuracy and CPU utilization. Our test results showed that when the sampling rate increases, both the accuracy and the CPU utilization become greater. Moreover, we observed that, for a fixed sampling rate, when window size increases, CPU utilization decreases.