IEEE Trans Neural Syst Rehabil Eng. 2023;31:4156-4166. doi: 10.1109/TNSRE.2023.3323390. Epub 2023 Oct 26.
While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient's voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient's active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median [Formula: see text]) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.
虽然康复机器人为改善苛刻临床环境中的早期动员治疗提供了急需的解决方案,但它们在患者监测方面也带来了新的挑战和机遇。除了量化患者在机器人辅助治疗运动中的自主贡献的基本挑战之外,由于时间和空间限制,许多传感器无法在临床环境中使用。在本文中,我们提出并比较了两种用于监测患者在运动中主动参与的指标。这两个指标均源自基本原理,具有相同的生物力学可解释性,即患者在机器人辅助动员治疗过程中的主动做功,但它们是在两个不同的空间(笛卡尔空间与肌肉空间)中计算的。此外,用于量化这两个指标的传感器彼此完全独立,并且相关测量值彼此无关。具体来说,基于机器人的功指标利用机器人集成的力传感器,而基于肌电图的功指标需要使用电生理传感器。然后,我们将这两个指标应用于使用经过临床认证的商业可用机器人系统进行的治疗,并将其与健康受试者的具体指令以及彼此进行比较。两个指标的输出都定性地符合健康受试者的预期行为。此外,两种指标之间显示出很强的相关性(中位数[公式:见正文]),不仅在健康受试者(n = 12)中,而且在患者(n = 2)中也是如此,为它们的有效性和可转移性提供了有力的证据。重要的是,基于机器人的功指标不依赖于机器人集成的传感器之外的任何传感器,因此非常适合在临床环境中应用。