Design and Implementation of Practical Step Detection Algorithm for Wrist-Worn Devices

In recent years, interest in wrist-worn devices has been growing, as market of wearable activity tracking devices have been enlarged. But, many wrist-worn devices have three main problems that activity tracking algorithms for wrist-worn devices should overcome: lack of sensor variety due to power consumption, low computing power, and noise from various sensor-carrying modes and walking velocities. This paper discusses an activity tracking, especially regarding step detection algorithm using three-axis accelerometer for wrist-worn devices. The proposed algorithm consists of three phases, which address the problems of wrist-worn devices. The first data preprocessing phase calculates the Euclidean norm of the acceleration vector. It enables the algorithm to track the movement of a device only with the acceleration data. The second data filtering phase reduces the noise with a simple digital low-pass filter. Then, the third peak detection phase adopts a sign-of-slope method and average threshold method to accurately detect the step peaks under different sensor-carrying modes and velocity conditions. A wrist-worn hardware prototype is designed and realized for algorithm evaluation. The experiment results show that the proposed algorithm is superior to the compared existing algorithm and commercial devices. The averaged detection error is approximately 1% in different test conditions.