Wearable Gait Authentication

With the wide use of wearable Internet of Things (IoT) devices, it is now possible to quickly gather information on human activities, including unconscious or subconscious acts. One such behavior is walking, which can provide individual patterns for each person and be utilized as a biometric feature for user authentication in healthcare systems. This paper proposes the lightweight seamless authentication framework (LiSA-G), which may be used to identify and authenticate users on commercial smart watches by leveraging statistical data and sensor data features related to human behavior. With a mean equal error rate (EER) of 8.2% and fewer features and less sensor data, the study's findings demonstrate that this approach delivers higher authentication accuracy. Such a strategy is also more realistic and can be deployed quickly due to the limited computational and energy capacity of wearable IoT systems in healthcare setting.

Перевод пока недоступен