| Summary: | 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.
|
|---|
