Air-writing via Receiver Array Based Ultrasonic Source Localization
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Machine Intelligence & kNowledge Engineering Lab
Permanent link to this recordhttp://hdl.handle.net/10754/662701
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AbstractAir-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 paper, 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 3-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.
CitationChen, H., Ballal, T., Muqaibel, A. H., Zhang, X., & Al-Naffouri, T. Y. (2020). Air-writing via Receiver Array Based Ultrasonic Source Localization. IEEE Transactions on Instrumentation and Measurement, 1–1. doi:10.1109/tim.2020.2991573
SponsorsThe authors would like to thank KAUST Visualization Core Lab for facilitating part of the experimental tests.