Facial electromyogram-based facial gesture recognition for hands-free control of an AR/VR environment: optimal gesture set selection and validation of feasibility as an assistive technology.

IF 3.2 4区 医学 Q2 ENGINEERING, BIOMEDICAL
Biomedical Engineering Letters Pub Date : 2023-04-11 eCollection Date: 2023-08-01 DOI:10.1007/s13534-023-00277-9
Chunghwan Kim, Chaeyoon Kim, HyunSub Kim, HwyKuen Kwak, WooJin Lee, Chang-Hwan Im
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引用次数: 0

Abstract

The rapid expansion of virtual reality (VR) and augmented reality (AR) into various applications has increased the demand for hands-free input interfaces when traditional control methods are inapplicable (e.g., for paralyzed individuals who cannot move their hands). Facial electromyogram (fEMG), bioelectric signals generated from facial muscles, could solve this problem. Discriminating facial gestures using fEMG is possible because fEMG signals vary with these gestures. Thus, these signals can be used to generate discrete hands-free control commands. This study implemented an fEMG-based facial gesture recognition system for generating discrete commands to control an AR or VR environment. The fEMG signals around the eyes were recorded, assuming that the fEMG electrodes were embedded into the VR head-mounted display (HMD). Sixteen discrete facial gestures were classified using linear discriminant analysis (LDA) with Riemannian geometry features. Because the fEMG electrodes were far from the facial muscles associated with the facial gestures, some similar facial gestures were indistinguishable from each other. Therefore, this study determined the best facial gesture combinations with the highest classification accuracy for 3-15 commands. An analysis of the fEMG data acquired from 15 participants showed that the optimal facial gesture combinations increased the accuracy by 4.7%p compared with randomly selected facial gesture combinations. Moreover, this study is the first to investigate the feasibility of implementing a subject-independent facial gesture recognition system that does not require individual user training sessions. Lastly, our online hands-free control system was successfully applied to a media player to demonstrate the applicability of the proposed system.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-023-00277-9.

基于面部肌电图的面部手势识别用于AR/VR环境的免提控制:最佳手势集选择和作为辅助技术的可行性验证。
虚拟现实(VR)和增强现实(AR)在各种应用中的快速扩展增加了对免提输入接口的需求,而传统的控制方法不适用(例如,对于无法移动手的瘫痪者)。面部肌电图(fEMG)是由面部肌肉产生的生物电信号,可以解决这个问题。使用fEMG辨别面部手势是可能的,因为fEMG信号随这些手势而变化。因此,这些信号可以用于生成离散的免提控制命令。本研究实现了一个基于fEMG的面部手势识别系统,用于生成离散命令来控制AR或VR环境。假设fEMG电极嵌入VR头戴式显示器(HMD),记录眼睛周围的fEMG信号。利用黎曼几何特征的线性判别分析(LDA)对16个离散的面部手势进行了分类。由于fEMG电极远离与面部手势相关的面部肌肉,一些相似的面部手势彼此无法区分。因此,本研究确定了3-15个命令分类准确率最高的最佳面部手势组合。对15名参与者的fEMG数据的分析表明,与随机选择的面部手势组合相比,最佳面部手势组合的准确率提高了4.7%p。此外,这项研究首次调查了实现不需要个人用户培训课程的独立于受试者的面部手势识别系统的可行性。最后,我们的在线免提控制系统成功地应用于媒体播放器,以证明所提出的系统的适用性。补充信息:在线版本包含补充材料,可访问10.1007/s13534-023-00277-9。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomedical Engineering Letters
Biomedical Engineering Letters ENGINEERING, BIOMEDICAL-
CiteScore
6.80
自引率
0.00%
发文量
34
期刊介绍: Biomedical Engineering Letters (BMEL) aims to present the innovative experimental science and technological development in the biomedical field as well as clinical application of new development. The article must contain original biomedical engineering content, defined as development, theoretical analysis, and evaluation/validation of a new technique. BMEL publishes the following types of papers: original articles, review articles, editorials, and letters to the editor. All the papers are reviewed in single-blind fashion.
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