Department of Electrical and Computer Engineering, Tufts University, 161 College Ave, Medford, MA, 02155, USA.
Nano Lab, Advanced Technology Laboratory, Tufts University, 200 Boston Ave, Medford, MA, 02155, USA.
Sci Rep. 2021 Jan 29;11(1):2646. doi: 10.1038/s41598-021-81284-7.
Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.
能够跟踪头部运动的人机界面将推动物理康复、增强现实/虚拟现实系统的发展，并有助于研究人类行为。本文提出了一种使用放置在个体颈部的薄柔性应变传感线来监测和分类头部位置的系统。一个由阻抗读取电路和蓝牙模块组成的无线电路模块记录并将应变信息传输到计算机。运动识别的数据处理算法提供了头部位置的近实时量化。输入数据经过滤波、归一化并分为数据段。从每个数据段中提取一组特征，并将其用作包括支持向量机、朴素贝叶斯和 KNN 在内的九个分类器的输入，以进行位置预测。对于一组九个头部方向，测试准确率达到了 92%左右。结果表明，这种人机界面平台准确、灵活、易于使用且具有成本效益。
