Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, Malaysia.
Int J Nanomedicine. 2011;6:3461-72. doi: 10.2147/IJN.S26619. Epub 2011 Dec 16.
The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
作者提出了一种通过神经活动和肌肉运动识别不同人类面部手势的新方法，可用于机器接口应用。人机界面 (HMI) 技术利用人类神经活动作为机器的输入控制器。最近，已经有很多工作致力于基于面部肌电图 (EMG) 的 HMI 的特定应用，这些应用仅使用有限且固定数量的面部手势。在这项工作中，提出了一种多用途接口，该接口可以支持 2-11 个控制命令，可应用于各种 HMI 系统。这项工作的意义在于找到最准确的面部手势，以最大的十一个控制命令应用于任何应用。从十位志愿者记录了十一个面部手势的肌电图。检测到的肌电图通过带通滤波器，并提取均方根特征。从现有的面部手势中，以不同的组中不同数量的手势，制作各种组合。最后，通过模糊 c-均值分类器对所有组合进行训练和分类。总之，选择了每组中具有最高识别精度的组合。证明所选组合的平均准确率>90%，可以用作命令控制器。
