Hand Dynamics for Behavioral User Authentication

We propose and evaluate a method to authenticate individuals by their unique hand dynamics, based on measurements from wearable sensors. Our approach utilises individual characteristics of hand movement when opening a door. We implement a sensor-fusion machine learning algorithm to classify individuals based on their hand movement and conduct a lab study with 20 participants to test the feasibility of the concept in the context of accessing physical doors as found in office buildings. Our results show that our approach yields an accuracy of 92% in classifying an individual and thus highlights the potential for behavioral hand dynamics for authentication.