A Random Forest-based Approach for Hand Gesture Recognition with Wireless Wearable Motion Capture Sensors

被引：10
|
作者：
Bargellesi, Nicolo [1 ]
Carletti, Mattia [1 ]
Cenedese, Angelo [1 ,2 ]
Susto, Gian Antonio [1 ,2 ]
Terzi, Matteo [1 ,2 ]
机构：
[1] Univ Padua, Dept Informat Engn DEI, Padua, Italy
[2] Univ Padua, Human Inspired Technol Ctr HIT, Padua, Italy
来源：
IFAC PAPERSONLINE | 2019年 / 52卷 / 11期
关键词：
Gesture Recognition; Machine Learning; Motion Capture; Random Forests;
D O I：
10.1016/j.ifacol.2019.09.129
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Gesture Recognition has a prominent importance in smart environment and home automation. Thanks to the availability of Machine Learning approaches it is possible for users to define gestures that can be associated with commands for the smart environment. In this paper we propose a Random Forest-based approach for Gesture Recognition of hand movements starting from wireless wearable motion capture data. In the presented approach, we evaluate different feature extraction procedures to handle gestures and data with different duration. To enhance reproducibility of our results and to foster research in the Gesture Recognition area, we share the dataset that we have collected and exploited for the present work. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：128 / 133
页数：6
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