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基于智能手表的上肢功能评估在阿联酋老年卒中人群中的应用:一项初步研究

Application of Smart Watch-Based Functional Evaluation for Upper Extremity Impairment: A Preliminary Study on Older Emirati Stroke Population.

作者信息

Kim Yeo Hyung, Kim Sarah, Nam Hyung Seok

机构信息

Department of Rehabilitation Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.

Department of Rehabilitation Medicine, Sheikh Khalifa Specialty Hospital, Ras al Khaimah 6365, United Arab Emirates.

出版信息

Sensors (Basel). 2025 Mar 3;25(5):1554. doi: 10.3390/s25051554.

Abstract

Smartwatch-based functional assessments for upper extremity movement are a promising tool for a detailed and serial assessment during stroke rehabilitation, but their clinical application remains challenging. In this study, nine patients with hemiparesis due to a stroke participated in occupational therapy sessions using virtual reality-based rehabilitation devices. An Action Research Arm Test (ARAT) was performed at baseline and after intervention, with wrist smartwatch sensors recording motion data. We extracted acceleration and gyro sensor data from smartwatches and calculated the average motion segment size (MSS) as a measure of motion smoothness. Among the included patients, four participants completed all 10 therapy sessions and the follow-up evaluation. The average MSSs of acceleration for all , , and directions were significantly correlated with the ARAT scores across all task domains. For angular motion, the average MSS in the gross movement task (domain 4) showed strong correlations with the ARAT scores: roll (r = 0.735, = 0.004), pitch (r = 0.715, = 0.009), and yaw (r = 0.704, = 0.007). At the serial follow-ups, most participants showed a considerable increase in the average MSSs of the roll, pitch, and yaw angles measured during domain 4, alongside improvements in their clinical ARAT scores. Our findings support the feasibility of using commercial smartwatch-based parameters for upper extremity functional evaluations during stroke rehabilitation and highlight their potential for serial follow-up assessments.

摘要

基于智能手表的上肢运动功能评估是中风康复期间进行详细和连续评估的一种有前途的工具,但其临床应用仍具有挑战性。在本研究中,9名中风后偏瘫患者参加了使用基于虚拟现实的康复设备的职业治疗课程。在基线和干预后进行了动作研究臂测试(ARAT),手腕智能手表传感器记录运动数据。我们从智能手表中提取了加速度和陀螺仪传感器数据,并计算了平均运动段大小(MSS)作为运动平滑度的指标。在所纳入的患者中,4名参与者完成了所有10次治疗课程和随访评估。所有x、y和z方向的加速度平均MSS与所有任务领域的ARAT评分显著相关。对于角运动,总体运动任务(领域4)中的平均MSS与ARAT评分显示出强相关性:横滚(r = 0.735,p = 0.004)、俯仰(r = 0.715,p = 0.009)和偏航(r = 0.704,p = 0.007)。在连续随访中,大多数参与者在领域4中测量的横滚、俯仰和偏航角的平均MSS有相当大的增加,同时他们的临床ARAT评分也有所改善。我们的研究结果支持在中风康复期间使用基于商业智能手表的参数进行上肢功能评估的可行性,并突出了它们在连续随访评估中的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2561/11902849/aacb1c8bc72d/sensors-25-01554-g001.jpg

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