ANN for Gesture Recognition using Accelerometer Data

被引：12

作者：

Miriam Lee-Cosio, Blanca ^{[1
]}

Delgado-Mata, Carlos ^{[1
]}

Ibanez, Jesus ^{[2
]}

机构：

[1] Univ Panamer, Escuela Ingn, IPIT, Campus Bonaterra, Aguascalientes 20290, Mexico

[2] Univ Pompeu Fabra Barcelona, Grup Tecnol Interact, Dept Informaci Tecnologias Commun, Barcelona, Spain

来源：

2012 IBEROAMERICAN CONFERENCE ON ELECTRONICS ENGINEERING AND COMPUTER SCIENCE | 2012年 / 3卷

关键词：

Pattern Recognition; Artificial Neural Networks; Wii remote; Gesture Recognition; Classification;

D O I：

10.1016/j.protcy.2012.03.012

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents the application of Artificial Neural Networks to recognise among gestures trajectory patterns in a Euclidean space. The data was filtered and normalised by the Fast Fourier Transform. The k-means algorithm was used to parametrise the optimized data as input of the ANN by creating 15 clusters of data. Using the FANN tool, the ANN was modeled trained and tested so that the output of the ANN is the recognised gesture. The raw data comes from a set of 8 trajectories representing gestures captured by a device based on accelerometers like the Nintendo Wii remote. (C) 2012 Published by Elsevier Ltd.

引用

页码：109 / 120

页数：12

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