Gesture recognition benchmark based on mobile phone

Abstract

Mobile phone plays an important role in our daily life. This paper develops a gesture recognition benchmark based on sensors of mobile phone. The built-in micro gyroscope and accelerometer of mobile phone can efficiently measure the accelerations and angular velocities along x-, y- and z-axis, which are used as the input data. We calculate the energy of the input data to reduce the effect of the phone’s posture variations. A large database is collected, which contains more than 1,000 samples of 8 gestures. The Hidden Markov Model (HMM), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are tested on the benchmark. The experimental results indicated that the employed methods can effectively recognize the gestures. To promote research on this topic, the source code and database are made available to the public. (mpl.buaa.edu.cn or correspondence author).