English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Wearable devices can gather sensitive information about their users. For this reason, automated\udauthentication and identification techniques are increasingly adopted to ensure security and privacy. Furthermore, identification can be used to automatically customize operations according to\udthe needs of the current user. A gait-based identification method that can be executed in real time\udon devices with limited resources is here presented. The method exploits a wearable accelerometer\udto continuously analyze the user’s gait pattern and perform identification. Experiments were conducted with 10 volunteers, who carried the device in a trouser pocket and followed their daily routine\udwithout predefined constraints. In total, ~98 hours of acceleration traces were collected in uncontrolled environment, including 3073 gait segments. User identification results show a recognition rate\udranging from 95% to 100%, depending on the mode of operation. It is demonstrated that the method\udcan be executed on a standalone device with <8 KB of RAM. In addition, the energy consumption is\udevaluated and compared with an architecture that requires the presence of an external computing unit. Results show that the proposed solution significantly improves the lifetime of the device (approximately +70% for the considered platform), hence fostering user acceptance

Real-Time Identification Using Gait Pattern Analysis on a Standalone Wearable Accelerometer