Human behavior and hand gesture classification for smart human-robot interaction

被引：28

作者：

Mendes, Nuno ^{[1
]}

Ferrer, Joao ^{[1
]}

Vitorino, Joao ^{[1
]}

Safeea, Mohammad ^{[1
]}

Neto, Pedro ^{[1
]}

机构：

[1] Univ Coimbra, Dept Mech Engn POLO 2, P-3030788 Coimbra, Portugal

来源：

27TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING, FAIM2017 | 2017年 / 11卷

关键词：

Human Robot Interaction; Human Behavior Recognition; Gestures; Segmentation; Accelerometer; REAL-TIME; RECOGNITION;

D O I：

10.1016/j.promfg.2017.07.156

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process. (C) 2017 The Authors. Published by Elsevier B.V.

引用

页码：91 / 98

页数：8

共 26 条

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