有线手套
手势
计算机科学
滑动窗口协议
手势识别
人工智能
线性判别分析
快速傅里叶变换
特征选择
窗口(计算)
计算机视觉
特征提取
压力传感器
模式识别(心理学)
语音识别
算法
工程类
机械工程
操作系统
作者
Granit Luzhnica,Jörg Simon,Elisabeth Lex,Viktoria Pammer
标识
DOI:10.1109/3dui.2016.7460035
摘要
This paper explores the recognition of hand gestures based on a data glove equipped with motion, bending and pressure sensors. We selected 31 natural and interaction-oriented hand gestures that can be adopted for general-purpose control of and communication with computing systems. The data glove is custom-built, and contains 13 bend sensors, 7 motion sensors, 5 pressure sensors and a magnetometer. We present the data collection experiment, as well as the design, selection and evaluation of a classification algorithm. As we use a sliding window approach to data processing, our algorithm is suitable for stream data processing. Algorithm selection and feature engineering resulted in a combination of linear discriminant analysis and logistic regression with which we achieve an accuracy of over 98.5% on a continuous data stream scenario. When removing the computationally expensive FFT-based features, we still achieve an accuracy of 98.2%.
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