MGRA: Motion Gesture Recognition via Accelerometer.

Feng Hong,Shujuan You,Zhongwen Guo,Yongtuo Zhang,Meiyu Wei

doi:10.3390/s16040530

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.

Highlights

The Micro-electromechanical Systems (MEMS) based accelerometer is one of the most commonly-used sensors for users to capture the posture, as well as the motion of devices [1].Extensive research has been carried out based on the accelerometer data of mobile devices, including phone placement recognition [2], knee joint angle measurement [3], indoor tracking [4] and physical activity recognition [5,6]
The objective of a motion gesture recognition system is to find out which gesture is intended by the users, which is a spatio-temporal pattern recognition problem
The results demonstrate that MGRA achieves an average accuracy of 95.83%

Summary

Introduction

The Micro-electromechanical Systems (MEMS) based accelerometer is one of the most commonly-used sensors for users to capture the posture, as well as the motion of devices [1]. Extensive research has been carried out based on the accelerometer data of mobile devices, including phone placement recognition [2], knee joint angle measurement [3], indoor tracking [4] and physical activity recognition [5,6]. Research conducted so far still faces a challenging problem, which is not tackled effectively: signal drift or the intrinsic noise of MEMS-based accelerometers on commercial mobile devices. The accelerometer enables a mobile device to “sense” how it is physically manipulated by the user. Except the problem of accelerometer signal drift or intrinsic noise, motion gesture recognition systems confront three new challenges as follows:

Objectives

Methods

Results

Conclusion