DocumentCode
3045276
Title
Individual classification through autoregressive modelling of micro-doppler signatures
Author
Garreau, Guillaume ; Nicolaou, Nicoletta ; Georgiou, Julius
Author_Institution
Holistic Electron. Res. Lab., Univ. of Cyprus, Nicosia, Cyprus
fYear
2012
fDate
28-30 Nov. 2012
Firstpage
312
Lastpage
315
Abstract
This paper introduces the use of autoregressive modelling (AR) to characterize individual human gait signatures from micro-Doppler data. AR models are fitted to micro-Doppler data obtained while 6 subjects walk towards a custom-made ultrasonic transceiver module. The estimated AR coefficients capture individual movement characteristics. Such features can be used to identify different subjects quickly and with low computational cost. In the best configuration, average performance higher than 98% was obtained.
Keywords
autoregressive processes; biomedical transducers; biomedical ultrasonics; gait analysis; transceivers; ultrasonic transducers; autoregressive coefficients; autoregressive modelling; human gait signatures; microDoppler signatures; ultrasonic transceiver module; Acoustics; Computational modeling; Data models; Humans; Legged locomotion; Radar; Transceivers; Micro-Doppler; autoregressive models; individual recognition; ultrasonic device;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Circuits and Systems Conference (BioCAS), 2012 IEEE
Conference_Location
Hsinchu
Print_ISBN
978-1-4673-2291-1
Electronic_ISBN
978-1-4673-2292-8
Type
conf
DOI
10.1109/BioCAS.2012.6418434
Filename
6418434
Link To Document