基于6DOF惯性传感器的头部手势识别

Ionut-Cristian Severin, D. Dobrea
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引用次数: 2

摘要

本文提出并研究了基于单惯性传感器和深度学习方法的头部手势识别思想。在本文中提出的实验中,主要目的是评估和确定能够准确识别头部运动的最佳深度学习模型。开发的系统可以识别八种头部手势活动。这项工作收集的数据来自9名年龄在20到40岁之间的志愿者,他们在耳机上安装了一个6DOF惯性传感器。结果表明,使用原始数据时,最佳模型的分类准确率达到了93.64%。在本实验中,每个头部手势指令的准确率在71.11%到100%之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Head Gesture Recognition based on 6DOF Inertial sensor using Artificial Neural Network
This paper proposed and investigated the head gesture recognition idea based on a single inertial sensor and deep learning approach. During the experiments presented in this paper, the main aim was to evaluate and determine the best deep learning models that can accurately recognize the head movements. The developed system can identify eight head gestures activities. The data collected in this work was done using a 6DOF inertial sensor placed on a headphone pair from 9 volunteers with ages between 20 and 40 years old. The results show that the best-proposed model's classification accuracy did reach a value equal to 93.64 % using raw data. In this experiment, each head gesture command's accuracy was in the range of 71.11 % to 100 %.
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