Air Writing via Receiver Array-Based Ultrasonic Source Localization

被引:66
|
作者
Chen, Hui [1 ]
Ballal, Tarig [1 ]
Muqaibel, Ali Hussein [2 ]
Zhang, Xiangliang [1 ]
Al-Naffouri, Tareq Y. [1 ]
机构
[1] King Abdullah Univ Sci & Technol KAUST, Div Comp Elect & Math Sci & Engn CEMSE, Thuwal 239556900, Saudi Arabia
[2] King Fahd Univ Petr & Minerals KFUPM, Dept Elect Engn, Dhahran 31261, Saudi Arabia
关键词
Acoustic localization; air writing; direction of arrival (DOA); human-machine interaction; phase difference; receiver arrays; sequence classification; tracking; DIFFERENCE AMBIGUITY RESOLUTION; GESTURE RECOGNITION; SIGNAL; LOCATION;
D O I
10.1109/TIM.2020.2991573
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Air-writing systems have recently been proposed as tools for human-machine interaction where instructions can be represented using letters or digits written in the air. Different technologies have been used to realize air-writing systems. In this article, we propose an air-writing system using acoustic waves. The proposed system consists of two components: a motion-tracking component and a text recognition component. For motion tracking, we utilize direction-of-arrival (DOA) information. An ultrasonic receiver array tracks the motion of a wearable ultrasonic transmitter by observing the change in the DOA of the signals. We propose a novel 2-D DOA estimation algorithm that can track the change in the direction of the transmitter using measured phase differences between the receiver array elements. The proposed phase-difference projection (PDP) algorithm can provide accurate tracking with a three-sensor receiver array. The motion-tracking information is passed next for text recognition. To this end, and in order to strike the desired balance between flexibility, processing speed, and accuracy, a training-free order-restricted matching (ORM) classifier is designed. The proposed air-writing system, which combines the proposed DOA estimation and text recognition algorithms, achieves a letter classification accuracy of 96.31%. The utility, processing time, and classification accuracy are compared with four training-free classifiers and two machine learning classifiers to demonstrate the efficiency of the proposed system.
引用
收藏
页码:8088 / 8101
页数:14
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