Real-time implementation of a non-invasive tongue-based human-robot interface
Real-time implementation of a non-invasive tongue-based human-robot interface
复制标题
实时实现基于舌头的非侵入式人机界面
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
L. Gupta
中科院分区：
文献类型：
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作者：
M. Mace;K. Mamun;R. Vaidyanathan;Shouyan Wang;L. Gupta
介绍了基于舌动耳压 (TMEP) 信号的辅助人机界面系统的实时实现，以及一系列模拟控制任务的结果。该系统在在线环境中的实施涉及短期能量计算、检测、分割和随后的信号分类，所有这些都必须根据之前的离线测试重新制定。这包括制定新的分类和特征提取方法。该方案利用离散余弦变换从时域信息中提取频率特征，利用单变量高斯最大似然分类器和两阶段交叉验证程序进行特征选择和提取。该分类器的性能与决策融合分类算法的实时实现一起展示，分别达到 96.28% 和 93.12%。系统测试考虑了误报信号的潜在分割。对平面轮椅的模拟映射命令展示了系统辅助机器人控制的能力。这是首次针对基于舌头的人机界面发布的实时结果，该界面不需要将传感器放置在口腔附近。
Real-time implementation of an assistive human-machine interface system based around tongue-movement ear pressure (TMEP) signals is presented, alongside results from a series of simulated control tasks. The implementation of this system into an online setting involves short-term energy calculation, detection, segmentation and subsequent signal classification, all of which had to be reformulated based on previous off-line testing. This has included the formulation of a new classification and feature extraction method. This scheme utilises the discrete cosine transform to extract the frequency features from the time domain information, a univariate Gaussian maximum likelihood classifier and a two phase cross-validation procedure for feature selection and extraction. The performance of this classifier is presented alongside a real-time implementation of the decision fusion classification algorithm, with each achieving 96.28% and 93.12% respectively. The system testing takes into consideration potential segmentation of false positive signals. A simulation mapping commands to a planar wheelchair demonstrates the capacity of the system for assistive robotic control. These are the first real-time results published for a tongue-based human-machine interface that does not require a transducer to be placed within the vicinity of the oral cavity.