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Non-verbal Communication and Touchless Activation of a Radio-Controlled Car via Facial Activity Recognition
International Journal of Precision Engineering and Manufacturing ( IF 2.6 ) Pub Date : 2020-02-24 , DOI: 10.1007/s12541-019-00291-x
Dong Yeol Han , Bi Oh Park , Jae Won Kim , Ji Hoon Lee , Won Gu Lee

Many smart glasses technologies are being developed to improve the working efficiency or quality of life in various fields. In some enterprises, these technologies are used to help improve the working quality and productivity and minimize data loss. In real life, smart glasses are applied as an entertainment device with augmented/virtual reality or as an assistive manipulator for the physically challenged. Thus, these technologies have mainly adopted various operating systems depending on usages, such as a touchpad, remote control, and voice recognition. However, conventional operating methods have limitations in non-verbal and noisy situations where people cannot use both hands. In this study, we present a method of detecting a facial signal for touchless activation using a transducer. We acquired a facial signal amplified by a lever mechanism using a load cell on the hinge of an eyewear. We then classified the signal and obtained their accuracy by calculating the confusion matrix with classified categories through a machine learning technique, i.e., the support vector machine. We can activate an actuator, such as a radio-controlled car, through a classified facial signal by using an eyewear-type signal transducer. Overall, our operating system can be useful for activating the actuator or transmitting a message through the classified facial activities in non-verbal situations and in situations where both hands cannot be used.



中文翻译:

基于面部活动识别的无线电控汽车的非语言交流和非接触式激活

人们正在开发许多智能眼镜技术,以提高各个领域的工作效率或生活质量。在某些企业中,这些技术用于帮助提高工作质量和生产率,并最大程度地减少数据丢失。在现实生活中,智能眼镜被用作具有增强/虚拟现实的娱乐设备,或作为残障人士的辅助操纵器。因此,这些技术主要根据用途采用了各种操作系统,例如触摸板,遥控器和语音识别。然而,常规的操作方法在人们不能用双手的非语言和嘈杂的情况下具有局限性。在这项研究中,我们提出了一种使用换能器检测用于非接触式激活的面部信号的方法。我们通过在眼镜铰链上使用称重传感器,通过杠杆机制放大了面部信号。然后,我们通过机器学习技术(即支持向量机)通过对分类类别的混淆矩阵进行计算来对信号进行分类并获得其准确性。我们可以使用眼镜式信号传感器通过分类的面部信号来激活执行器,例如无线电遥控汽车。总体而言,我们的操作系统对于非言语场合和双手无法使用的情况,对于通过分类的面部活动来激活执行器或传输信息非常有用。支持向量机。我们可以使用眼镜式信号传感器通过分类的面部信号来激活执行器,例如无线电遥控汽车。总体而言,我们的操作系统对于非言语场合和双手无法使用的情况,对于通过分类的面部活动来激活执行器或传输信息非常有用。支持向量机。我们可以使用眼镜式信号传感器通过分类的面部信号来激活执行器,例如无线电遥控汽车。总体而言,我们的操作系统对于非言语场合和双手无法使用的情况,对于通过分类的面部活动来激活执行器或传输信息非常有用。

更新日期:2020-02-24
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