Xu Jingxi, Meeker Cassie, Chen Ava, Winterbottom Lauren, Fraser Michaela, Park Sangwoo, Weber Lynne M, Miya Mitchell, Nilsen Dawn, Stein Joel, Ciocarlie Matei
Department of Computer Science, Columbia University, New York, NY 10027, USA.
Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA.
IEEE Int Conf Robot Autom. 2022 May;2022:8097-8103. doi: 10.1109/icra46639.2022.9811932. Epub 2022 Jul 12.
In order to provide therapy in a functional context, controls for wearable robotic orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG-based method to operate a robotic hand orthosis, but the process of training a control that is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for controlling a powered hand orthosis for stroke subjects. To the best of our knowledge, this is the first use of semi-supervised learning for an orthotic application. Specifically, we propose a disagreement-based semi-supervision algorithm for handling intrasession concept drift based on multimodal ipsilateral sensing. We evaluate the performance of our algorithm on data collected from five stroke subjects. Our results show that the proposed algorithm helps the device adapt to intrasession drift using unlabeled data and reduces the training burden placed on the user. We also validate the feasibility of our proposed algorithm with a functional task; in these experiments, two subjects successfully completed multiple instances of a pick-and-handover task.
为了在功能环境中提供治疗，可穿戴机器人矫形器的控制需要强大且直观。我们之前介绍了一种直观的、用户驱动的、基于肌电图的方法来操作机器人手部矫形器，但训练一种对概念漂移（输入信号的变化）具有鲁棒性的控制过程给用户带来了沉重负担。在本文中，我们探索将半监督学习作为一种为中风患者控制动力手部矫形器的范例。据我们所知，这是半监督学习在矫形应用中的首次使用。具体而言，我们提出了一种基于分歧的半监督算法，用于基于多模态同侧传感处理会话内概念漂移。我们在从五名中风患者收集的数据上评估了我们算法的性能。我们的结果表明，所提出的算法有助于设备使用未标记数据适应会话内漂移，并减轻了用户的训练负担。我们还通过一项功能任务验证了我们提出算法的可行性；在这些实验中，两名受试者成功完成了多次拾取和交接任务的实例。
