Personalized Gait-based Authentication Using UWB Wearable Devices
| Autor: | Simone Tavoletta, Matteo Zagaglia, Alessio Bianchini, Adriano Arra, Pietro Ciravolo, Martina Olivelli, Alessio Vecchio, Joana Chavez, Gabriele Scoma, Fatjon Nebiu |
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| Rok vydání: | 2019 |
| Předmět: |
Authentication
User authentication business.industry Computer science Ultra-wideband 020206 networking & telecommunications 02 engineering and technology Set (abstract data type) Gait (human) 0202 electrical engineering electronic engineering information engineering Classification methods 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Wearable technology Prior information |
| Zdroj: | UMAP |
| DOI: | 10.1145/3320435.3320473 |
| Popis: | Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated. |
| Databáze: | OpenAIRE |
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