Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals

Handle URI:
http://hdl.handle.net/10754/626599
Title:
Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals
Authors:
Chen, Hui; Ballal, Tarig; Saad, Mohamed; Al-Naffouri, Tareq Y. ( 0000-0003-2843-5084 )
Abstract:
Hand gestures are tools for conveying information, expressing emotion, interacting with electronic devices or even serving disabled people as a second language. A gesture can be recognized by capturing the movement of the hand, in real time, and classifying the collected data. Several commercial products such as Microsoft Kinect, Leap Motion Sensor, Synertial Gloves and HTC Vive have been released and new solutions have been proposed by researchers to handle this task. These systems are mainly based on optical measurements, inertial measurements, ultrasound signals and radio signals. This paper proposes an ultrasonic-based gesture recognition system using AOA (Angle of Arrival) information of ultrasonic signals emitted from a wearable ultrasound transducer. The 2-D angles of the moving hand are estimated using multi-frequency signals captured by a fixed receiver array. A simple redundant dictionary matching classifier is designed to recognize gestures representing the numbers from `0' to `9' and compared with a neural network classifier. Average classification accuracies of 95.5% and 94.4% are obtained, respectively, using the two classification methods.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Chen H, Ballal T, Saad M, Al-Naffouri TY (2017) Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals. 2017 25th European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.23919/eusipco.2017.8081160.
Publisher:
IEEE
Journal:
2017 25th European Signal Processing Conference (EUSIPCO)
KAUST Grant Number:
OSR-2015-Sensors-2700
Issue Date:
2-Nov-2017
DOI:
10.23919/eusipco.2017.8081160
Type:
Conference Paper
Sponsors:
This work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.
Additional Links:
http://ieeexplore.ieee.org/document/8081160/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Huien
dc.contributor.authorBallal, Tarigen
dc.contributor.authorSaad, Mohameden
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2018-01-01T12:19:02Z-
dc.date.available2018-01-01T12:19:02Z-
dc.date.issued2017-11-02en
dc.identifier.citationChen H, Ballal T, Saad M, Al-Naffouri TY (2017) Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals. 2017 25th European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.23919/eusipco.2017.8081160.en
dc.identifier.doi10.23919/eusipco.2017.8081160en
dc.identifier.urihttp://hdl.handle.net/10754/626599-
dc.description.abstractHand gestures are tools for conveying information, expressing emotion, interacting with electronic devices or even serving disabled people as a second language. A gesture can be recognized by capturing the movement of the hand, in real time, and classifying the collected data. Several commercial products such as Microsoft Kinect, Leap Motion Sensor, Synertial Gloves and HTC Vive have been released and new solutions have been proposed by researchers to handle this task. These systems are mainly based on optical measurements, inertial measurements, ultrasound signals and radio signals. This paper proposes an ultrasonic-based gesture recognition system using AOA (Angle of Arrival) information of ultrasonic signals emitted from a wearable ultrasound transducer. The 2-D angles of the moving hand are estimated using multi-frequency signals captured by a fixed receiver array. A simple redundant dictionary matching classifier is designed to recognize gestures representing the numbers from `0' to `9' and compared with a neural network classifier. Average classification accuracies of 95.5% and 94.4% are obtained, respectively, using the two classification methods.en
dc.description.sponsorshipThis work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8081160/en
dc.titleAngle-of-arrival-based gesture recognition using ultrasonic multi-frequency signalsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2017 25th European Signal Processing Conference (EUSIPCO)en
kaust.authorChen, Huien
kaust.authorBallal, Tarigen
kaust.authorSaad, Mohameden
kaust.authorAl-Naffouri, Tareq Y.en
kaust.grant.numberOSR-2015-Sensors-2700en
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