In this paper, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated robotic processes (i.e. words are analogous to a teleoperator's gestures, sentences are analogous to the entire teleoperated task or process) and implement HMMs to model the teleoperated task. To test the continuous authentication performance of the proposed method, we conducted two sets of analyses. We built a virtual reality (VR) experimental environment using a commodity VR headset (HTC Vive) and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a real teleoperated task. An experimental study with 10 subjects was then conducted. We also performed simulated continuous operator authentication by using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The performance of the model was evaluated based on the continuous (real-time) operator authentication accuracy as well as resistance to a simulated impersonation attack. The results suggest that the proposed method is able to achieve 70% (VR experiment) and 81% (JIGSAW dataset) continuous classification accuracy with as short as a 1-second sample window. It is also capable of detecting the impersonation attack in real-time.
翻译:在本文中,我们介绍了基于隐藏Markov 模型(HMMM)的远程操作机器人流程中连续操作者认证操作者持续操作者持续认证的新做法。虽然HMM公司最初是开发并广泛用于语音识别,但HMM公司在人文运动和活动模型中表现得非常出色。我们将人文和远程操作机器人流程(即词类类似于远程操作者的手势,句类比整个远程操作任务或流程)之间的类比,并采用HMMM公司模拟远程操作任务。为了测试拟议方法的持续认证性能,我们进行了两套分析。我们建立了虚拟现实(VR)实验环境,我们使用一种商品VR头(HTC Viveve)和不便反馈使控制器(Sensable PHANToM Omni)能够模拟真正的远程操作任务。随后进行了10个主题的实验性研究,我们还使用JHUHU-ISI Gestur和Skill Asir Supid Serview 任务。模型的性评估性评估性根据连续(实时) 实时(实时) 实时) 实时操作操作者认证结果和持续操作操作者认证结果建议,将81ABIAVADR的准确性认证作为模拟数据作为模拟数据作为模拟的模拟方法,作为模拟数据。