Speed-independent gait identification for mobile devices

Bailador del Pozo, Gonzalo, Sánchez Ávila, María del Carmen ORCID: https://orcid.org/0000-0002-7690-1011, Santos Sierra, Alberto de and Guerra Casanova, Javier (2012). Speed-independent gait identification for mobile devices. "International Journal of Pattern Recognition and Artificial Intelligence", v. 26 (n. 8); pp. 126001301-126001313. ISSN 0218-0014. https://doi.org/10.1142/S0218001412600130.

Descripción

Título: Speed-independent gait identification for mobile devices
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Pattern Recognition and Artificial Intelligence
Fecha: Diciembre 2012
ISSN: 0218-0014
Volumen: 26
Número: 8
Materias:
ODS:
Palabras Clave Informales: Biometrics, gait, accelerometer, dynamic time warping
Escuela: Centro de Domótica Integral (CeDInt) (UPM)
Departamento: Aeronaves y Vehículos Espaciales
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di±culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.