3D human gesture capturing and recognition by the IMMU-based data glove
3D human gesture capturing and recognition by the IMMU-based data glove
复制标题
基于 IMMU 的数据手套进行 3D 人体手势捕捉和识别
DOI：
10.1016/j.neucom.2017.02.101
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发表时间：
2018-02-14
期刊：
影响因子：
6
通讯作者：
Liu, Chunfang
中科院分区：
文献类型：
--
作者：
Fang, Bin;Sun, Fuchun;Liu, Chunfang
手势识别为人机交互提供了一种智能、自然、便捷的方式。提出了一种基于三轴陀螺仪、三轴加速度计和三轴磁强计组成的惯性和磁力测量单元（IMMU）的手势采集与识别数据手套。所提出的数据手套有18个低成本的IMMU，这些IMMU结构紧凑，尺寸小到足以佩戴。手势包括手臂、手掌和手指的三维运动都被数据手套完全捕获。同时，我们尝试使用极端学习机（ELM）的手势识别，这还没有发现在相关的应用。分别提出了基于ELM的静态手势和动态手势识别方法。手势捕捉和识别的实验结果验证了所提方法的有效性。(C)2017爱思唯尔B.V.保留所有权利。
Gestures recognition provides an intelligent, natural, and convenient way for human-robot interaction (HRI). This paper presents a novel data glove for gestures capturing and recognition based on inertial and magnetic measurement units (IMMUs), which are made up of three-axis gyroscopes, three-axis accelerometers and three-axis magnetometers. The proposed data glove has eighteen low-cost IMMUs, which are compact and small enough to wear. The gestures included the three-dimensional motions of arm, palm and fingers are completely captured by the data glove. Meanwhile, we attempt to use extreme learning machine (ELM) for gesture recognition which has not found yet in the relevant application. The ELM-based recognition methods for both static gestures and dynamic gestures are respectively presented. The experimental results of gestures capturing and recognition verify the effectiveness of the proposed methods. (C) 2017 Elsevier B.V. All rights reserved.