Title: Personalised gesture recognition based on tri-axis accelerometer using Gabor filters

Authors: Zhenyu He

Addresses: College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, 510632, China

Abstract: Human gesture is one of the most important ingredients of context information. In this paper, a novel gesture recognition framework based on tri-axis accelerometer mounted on a cell phone is proposed. Since the length of acceleration signals is variable according to different gestures and different subjects' input speed, most of recognition algorithms cannot be used. To solve this problem, we propose a novel resampling method by combining decimation and interpolation. After that, we propose 1D Gabor coefficients of acceleration signals as features. However, the dimensionality of Gabor feature space used in gesture recognition is very high. We adopt Boosting and a two-stage method PCA plus LDA to select and compress the Gabor feature. The classifier we used is fast multi-class support vector machine. The average recognition results of 17 complex gestures using the proposed Gabor feature are effective. The performance of experimental results shows that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile devices.

Keywords: gesture recognition; tri-axis accelerometer; Gabor filter; boosting; PCA; LDA.

DOI: 10.1504/IJAHUC.2020.107820

International Journal of Ad Hoc and Ubiquitous Computing, 2020 Vol.34 No.2, pp.92 - 101

Received: 18 Mar 2019
Accepted: 31 Aug 2019

Published online: 17 Jun 2020 *

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