Continuous User Authentication for Human-Robot Collaboration

Abstract

Human-robot collaboration is on the increase and having a major impact on areas such as manufacturing, where the abilities of the human worker, augmented by those of the robot, bring increased flexibility and performance. However, close collaboration, including physical interaction, brings with it complex safety and security issues that were previously mitigated by human-robot segregation and isolated control networks. Exoskeletons pose a particularly interesting case whereby physical coupling of the user and robot is required throughout operation. We envisage the use of continuous authentication to exoskeletons, i.e. to ensure a user is who they claim to be, and that they have sufficient authority to operate the device for the duration of its use. In this paper we demonstrate such an approach to behavioural biometrics using data acquired through wearable sensors (hand manipulations recorded by a sensorised glove) while the user performs a selection of industrial tasks, including handling loads and inserting screws. The results show that the approach can discriminate between users with a low Equal Error Rate (EER; <3% in the worst case analysed). We believe that such an approach will also benefit other applications where wearables are used in robot control, such as in tele-operation.