Tesfazgi Samuel, Sangouard Ronan, Endo Satoshi, Hirche Sandra
Chair of Information-oriented Control (ITR), TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Front Neurorobot. 2023 Aug 24;17:1167604. doi: 10.3389/fnbot.2023.1167604. eCollection 2023.
Providing high degree of personalization to a specific need of each patient is invaluable to improve the utility of robot-driven neurorehabilitation. For the desired customization of treatment strategies, precise and reliable estimation of the patient's state becomes important, as it can be used to continuously monitor the patient during training and to document the rehabilitation progress. Wearable robotics have emerged as a valuable tool for this quantitative assessment as the actuation and sensing are performed on the joint level. However, upper-limb exoskeletons introduce various sources of uncertainty, which primarily result from the complex interaction dynamics at the physical interface between the patient and the robotic device. These sources of uncertainty must be considered to ensure the correctness of estimation results when performing the clinical assessment of the patient state. In this work, we analyze these sources of uncertainty and quantify their influence on the estimation of the human arm impedance. We argue that this mitigates the risk of relying on overconfident estimates and promotes more precise computational approaches in robot-based neurorehabilitation.
为每个患者的特定需求提供高度个性化对于提高机器人驱动的神经康复效用非常重要。为了实现所需的治疗策略定制，准确可靠地估计患者状态变得至关重要，因为它可用于在训练期间持续监测患者并记录康复进展。可穿戴机器人作为一种有价值的工具已出现用于这种定量评估，因为驱动和传感是在关节层面进行的。然而，上肢外骨骼引入了各种不确定性来源，这主要源于患者与机器人设备之间物理界面处的复杂相互作用动力学。在对患者状态进行临床评估时，必须考虑这些不确定性来源以确保估计结果的正确性。在这项工作中，我们分析这些不确定性来源并量化它们对人体手臂阻抗估计的影响。我们认为这降低了依赖过度自信估计的风险，并促进了基于机器人的神经康复中更精确的计算方法。
