TY - GEN
T1 - Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals
AU - Chen, Hui
AU - Ballal, Tarig
AU - Saad, Mohamed
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.
PY - 2017/11/2
Y1 - 2017/11/2
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/626599
UR - http://ieeexplore.ieee.org/document/8081160/
UR - http://www.scopus.com/inward/record.url?scp=85041397158&partnerID=8YFLogxK
U2 - 10.23919/eusipco.2017.8081160
DO - 10.23919/eusipco.2017.8081160
M3 - Conference contribution
AN - SCOPUS:85041397158
SN - 9780992862671
SP - 16
EP - 20
BT - 2017 25th European Signal Processing Conference (EUSIPCO)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -