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整合步态触发式混合现实与神经生理监测作为下一代门诊卒中康复的框架

Integrated Gait Triggered Mixed Reality and Neurophysiological Monitoring as a Framework for Next-Generation Ambulatory Stroke Rehabilitation.

作者信息

Ko Li-Wei, Stevenson Cory, Chang Wei-Chiao, Yu Kuen-Han, Chi Kai-Chiao, Chen Yi-Jen, Chen Chia-Hsin

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2021;29:2435-2444. doi: 10.1109/TNSRE.2021.3125946. Epub 2021 Nov 25.

DOI:10.1109/TNSRE.2021.3125946
PMID:34748494
Abstract

Brain stroke affects millions of people in the world every year, with 50 to 60 percent of stroke survivors suffering from functional disabilities, for which early and sustained post-stroke rehabilitation is highly recommended. However, approximately one third of stroke patients do not receive early in hospital rehabilitation programs due to insufficient medical facilities or lack of motivation. Gait triggered mixed reality (GTMR) is a cognitive-motor dual task with multisensory feedback tailored for lower-limb post-stroke rehabilitation, which we propose as a potential method for addressing these rehabilitation challenges. Simultaneous gait and EEG data from nine stroke patients was recorded and analyzed to assess the applicability of GTMR to different stroke patients, determine any impacts of GTMR on patients, and better understand brain dynamics as stroke patients perform different rehabilitation tasks. Walking cadence improved significantly for stroke patients and lower-limb movement induced alpha band power suppression during GTMR tasks. The brain dynamics and gait performance across different severities of stroke motor deficits was also assessed; the intensity of walking induced event related desynchronization (ERD) was found to be related to motor deficits, as classified by Brunnstrom stage. In particular, stronger lower-limb movement induced ERD during GTMR rehabilitation tasks was found for patients with moderate motor deficits (Brunnstrom stage IV). This investigation demonstrates the efficacy of the GTMR paradigm for enhancing lower-limb rehabilitation, explores the neural activities of cognitive-motor tasks in different stages of stroke, and highlights the potential for joining enhanced rehabilitation and real-time neural monitoring for superior stroke rehabilitation.

摘要

每年全球有数百万人遭受脑中风影响,50%至60%的中风幸存者存在功能障碍,因此强烈建议在中风后进行早期且持续的康复治疗。然而,约三分之一的中风患者由于医疗设施不足或缺乏积极性,未在医院接受早期康复治疗。步态触发混合现实(GTMR)是一种认知-运动双重任务,具有为中风后下肢康复量身定制的多感官反馈,我们将其作为应对这些康复挑战的一种潜在方法提出。记录并分析了9名中风患者同时的步态和脑电图数据,以评估GTMR对不同中风患者的适用性,确定GTMR对患者的任何影响,并更好地理解中风患者执行不同康复任务时的脑动力学。中风患者的步行节奏显著改善,并且在GTMR任务期间下肢运动引起了α波段功率抑制。还评估了不同严重程度的中风运动缺陷患者的脑动力学和步态表现;发现步行诱发的事件相关去同步化(ERD)强度与运动缺陷有关,运动缺陷由Brunnstrom分期分类。特别是,在GTMR康复任务期间,中度运动缺陷(Brunnstrom分期IV期)患者的下肢运动引起的ERD更强。这项研究证明了GTMR模式对增强下肢康复的有效性,探索了中风不同阶段认知-运动任务的神经活动,并强调了将强化康复与实时神经监测相结合以实现卓越中风康复的潜力。

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A larger augmented-reality field of view improves interaction with nearby holographic objects.
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PLoS One. 2024 Oct 21;19(10):e0311804. doi: 10.1371/journal.pone.0311804. eCollection 2024.
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