Open AccessMethod based on UWB for user identification during gait periods
Author(s) -
Vecchio Alessio,
Cola Guglielmo
Publication year - 2019
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5050
Subject(s) - computer science , biometrics , gait , identification (biology) , wearable computer , gait analysis , inertial measurement unit , artificial intelligence , accelerometer , wearable technology , computer vision , embedded system , physical medicine and rehabilitation , medicine , biology , operating system , botany
Everyone has a different way of walking, and for this reason, gait has been studied in the last few years as an important biometric information source. This study explores a novel approach, based on ultra‐wideband (UWB) technology, for user identification via gait analysis. In the proposed method, the user is supposed to wear two or more devices embedding a UWB transceiver. During gait, the distances between the devices are estimated via UWB and then analysed by means of a machine learning classifier, which provides automatic identification. Experiments were carried out by 12 volunteers, who walked while wearing four UWB boards (placed on the head, wrist, ankle, and in a trouser pocket). The off‐line evaluation considered a set of different possible configurations in terms of number and position of the wearable devices. Despite a relatively low sampling frequency of 10 Hz, the results are promising: average identification accuracy is as high as ∼96% with four devices, and above 90% with three devices (wrist, trouser pocket, and ankle). This novel approach may enhance the accuracy of inertial‐based systems for continuous user identification.