Real-time identification using gait pattern analysis on a standalone wearable accelerometer

Wearable devices can gather sensitive information about their users. For this reason, automated authentication and identification techniques are increasingly adopted to ensure security and privacy. Furthermore, identification can be used to automatically customize operations according to the needs of the current user. A gait-based identification method that can be executed in real time on devices with limited resources is here presented. The method exploits a wearable accelerometer to 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 without 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 ranging from 95% to 100%, depending on the mode of operation. It is demonstrated that the method can be executed on a standalone device with <8 KB of RAM. In addition, the energy consumption is evaluated 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.