title: MGRA: Motion Gesture Recognition via Accelerometer.
abstract: Accelerometers have been widely embedded in most current mobile devices, enabling easy and intuitive operations. This paper proposes a Motion Gesture Recognition system (MGRA) based on accelerometer data only, which is entirely implemented on mobile devices and can provide users with real-time interactions. A robust and unique feature set is enumerated through the time domain, the frequency domain and singular value decomposition analysis using our motion gesture set containing 11,110 traces. The best feature vector for classification is selected, taking both static and mobile scenarios into consideration. MGRA exploits support vector machine as the classifier with the best feature vector. Evaluations confirm that MGRA can accommodate a broad set of gesture variations within each class, including execution time, amplitude and non-gestural movement. Extensive evaluations confirm that MGRA achieves higher accuracy under both static and mobile scenarios and costs less computation time and energy on an LG Nexus 5 than previous methods.
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China. hongfeng@ouc.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Hong
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China. youshujuan@gmail.com.
authors.forename: Shujuan
authors.initials: S
authors.lastname: You
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China. weimeiyu90@163.com.
authors.forename: Meiyu
authors.initials: M
authors.lastname: Wei
authors.affiliation: School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia. ytzhang@cse.unsw.edu.au.
authors.forename: Yongtuo
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China. guozhw@ouc.edu.cn.
authors.forename: Zhongwen
authors.initials: Z
authors.lastname: Guo
keywords: SVM; accelerometer; feature selection; gesture recognition