arXiv 2504.192302025-09-20cs.ROcs.SYeess.SY

用于神经系统疾病康复的机器人路径制作平台：临床用例

Robotic Trail Maker Platform for Rehabilitation in Neurological Conditions: Clinical Use Cases

OSF HealthCare Outpatient Rehabilitation（OSF医疗保健门诊康复中心）；University of Belgrade（贝尔格莱德大学）；University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Ljubljana（卢布尔雅那大学）；Zhejiang University（浙江大学）；Aalto University（阿尔托大学）

Srikar Annamraju, Harris Nisar, Dayu Xia, Shankar A. Deka, Anne Horowitz, Nadica Miljković, Dušan M. Stipanović

备注：: The first three authors are co-first authors. This manuscript is under review with the IEEE Transactions on Neural Systems and Rehabilitation Engineering

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出一种结合临床连线测试的机器人康复平台，既能评估又能训练神经系统疾病患者，并通过动态辅助显著提升患者速度，同时利用神经网络进行分类和运动预测。

摘要：

患有神经系统疾病的患者需要康复以恢复其运动、视觉和认知能力。为解决治疗师短缺问题并减轻其工作负担，提出了一种涉及临床连线测试（Trail Making Test）的机器人康复平台。治疗师可以为每位患者创建自定义路径，患者可以使用机器人设备沿路径进行追踪。该平台能够追踪患者的表现，并利用这些数据通过机器人与患者接口提供动态辅助。因此，所提出的平台不仅作为评估平台发挥作用，还能训练患者进行恢复。该开发平台已在一家康复中心得到验证，由治疗师和患者操作该设备。结果发现，与健康受试者相比，患者在使用该平台时表现较差，而所提供的辅助也改善了患者的表现。统计分析表明，在机器人辅助下，患者的速度得到了显著提升。此外，还训练了神经网络，利用收集到的数据对患者和健康受试者进行分类，并预测他们的运动。

英文摘要：

Patients with neurological conditions require rehabilitation to restore their motor, visual, and cognitive abilities. To meet the shortage of therapists and reduce their workload, a robotic rehabilitation platform involving the clinical trail making test is proposed. Therapists can create custom trails for each patient and the patient can trace the trails using a robotic device. The platform can track the performance of the patient and use these data to provide dynamic assistance through the robot to the patient interface. Therefore, the proposed platform not only functions as an evaluation platform, but also trains the patient in recovery. The developed platform has been validated at a rehabilitation center, with therapists and patients operating the device. It was found that patients performed poorly while using the platform compared to healthy subjects and that the assistance provided also improved performance amongst patients. Statistical analysis demonstrated that the speed of the patients was significantly enhanced with the robotic assistance. Further, neural networks are trained to classify between patients and healthy subjects and to forecast their movements using the data collected.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。