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索勒曼手部功能测验“用钢笔书写”:康复评估中的计算机视觉方法。

Sollerman Hand Function Sub-Test "Write with a Pen": A Computer-Vision-Based Approach in Rehabilitation Assessment.

机构信息

Communication Networks and Applications Laboratory (CNALab), Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece.

出版信息

Sensors (Basel). 2023 Jul 17;23(14):6449. doi: 10.3390/s23146449.

Abstract

Impaired hand function is one of the most frequently persistent consequences of stroke. Throughout the rehabilitation process, physicians consistently monitor patients and perform kinematic evaluations in order to assess their overall progress in motor recovery. The Sollerman Hand Function Test (SHT) is a valuable assessment tool used to evaluate a patient's capacity to engage in daily activities. It holds great importance in the field of medicine as it aids in the assessment of treatment effectiveness. Nevertheless, the requirement for a therapist's physical presence and the use of specialized materials make the test time-consuming and reliant on clinic availability. In this paper, we propose a computer-vision-based approach to the "Write with a pen" sub-test, originally included in the SHT. Our implementation does not require extra hardware equipment and is able to run on lower-end hardware specifications, using a single RGB camera. We have incorporated all the original test's guidelines and scoring methods into our application, additionally providing an accurate hand spasticity evaluator. After briefly presenting the current research approaches, we analyze and demonstrate our application, as well as discuss some issues and limitations. Lastly, we share some preliminary findings from real-world application usage conducted at the University campus and outline our future plans.

摘要

手部功能障碍是中风后最常见的持续后果之一。在整个康复过程中,医生会持续监测患者并进行运动学评估,以评估他们在运动恢复方面的整体进展。Sollerman 手功能测试(SHT)是一种评估患者日常活动能力的有价值的评估工具。它在手疗法领域具有重要意义,因为它有助于评估治疗效果。然而,该测试需要治疗师的亲自到场和使用专门的材料,因此耗时且依赖诊所的可用性。在本文中,我们提出了一种基于计算机视觉的方法来实现 SHT 中的“用钢笔书写”子测试。我们的实现不需要额外的硬件设备,并且能够在低端硬件规格上运行,仅使用单个 RGB 摄像机。我们已经将原始测试的所有指南和评分方法都纳入了我们的应用程序中,此外还提供了一个准确的手部痉挛评估器。在简要介绍当前的研究方法之后,我们分析并展示了我们的应用程序,并讨论了一些问题和限制。最后,我们分享了在大学校园进行的实际应用中的一些初步发现,并概述了我们的未来计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a803/10383357/c9edcb8cf5ed/sensors-23-06449-g001.jpg

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