Method based on UWB for user identification during gait periods

• Main Authors: Alessio Vecchio, Guglielmo Cola
• Format: Article
• Language: English
• Published: Wiley 2019-05-01
• Series: Healthcare Technology Letters
• Subjects: ultra wideband communication; biometrics (access control); transceivers; gait analysis; learning (artificial intelligence); interactive devices; gait periods; ultra-wideband technology; uwb transceiver; machine learning classifier; uwb boards; trouser pocket; wearable devices; inertial-based systems; continuous user identification; biometric information source; frequency 10.0 hz
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5050

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.