4.7 Article

Air Writing via Receiver Array-Based Ultrasonic Source Localization

Journal

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 69, Issue 10, Pages 8088-8101

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.2991573

Keywords

Acoustic localization; air writing; direction of arrival (DOA); human-machine interaction; phase difference; receiver arrays; sequence classification; tracking

Funding

  1. King Abdullah University of Science and Technology (KAUST)'s Office of Sponsored Research [OSR-2016-KKI-2899]

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available