An Evidence-Based Intelligent Method for Upper-Limb Motor Assessment via a VR Training System on Stroke Rehabilitation
An Evidence-Based Intelligent Method for Upper-Limb Motor Assessment via a VR Training System on Stroke Rehabilitation
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DOI：
10.1109/access.2021.3075778
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Yeh, Shih-Ching
中科院分区：
文献类型：
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作者：
Lee, Si-Huei;Cui, Jianjun;Yeh, Shih-Ching
近年来，虚拟现实技术（virtual-reality，VR）作为一种新兴技术，被临床治疗师广泛应用于临床运动康复训练中，为运动康复提供丰富的训练任务。与此同时，沿着传感技术作为与虚拟环境交互的手段的进步，大量的数据，如运动轨迹，足部压力或肌电图，通过基于VR的运动训练任务被测量，并被认为是功能评估的重要线索。然而，很少有研究彻底应用基于传感器的数据进行运动评估，而是高度依赖评估量表，如TEMPA或Fugl-Meyer。在本研究中，提出一个虚拟现实上肢运动训练系统，用于脑卒中康复。对22例脑卒中患者进行了临床试验，以检验拟议VR系统的有效性。此外，提出了各种运动指标，通过运动轨迹。在此基础上，结合运动轨迹、任务表现和评价量表等多模型数据，应用机器学习方法建立基于证据的上肢运动功能评价模型。结果表明，所提出的VR系统是显着有效的运动康复。此外，一些运动指标被发现有显着差异的试验前和试验后，并高度相关的评价量表。最后，通过对多模型数据的融合，机器学习评估模型的准确率达到92.72%，显示了其巨大的临床应用潜力。
Recently, virtual-reality (VR) has been an emerging technology, to this regard, it is widely employed by therapists to provide rich training tasks for the purpose of motor rehabilitation in clinics. Meanwhile, along with the progress of sensing technologies as means for the interaction with virtual environment, a large amount of data, such as motor trajectory, foot pressure or electromyography, is measured via VR-based motor training tasks and is considered as important clues for functional evaluations. However, very few study thoroughly applied the sensor-based data for motor assessment, instead, evaluation scales, such as TEMPA or Fugl-Meyer, were highly relied. In this study, a VR upper-limb motor training system was proposed for stroke rehabilitation. Clinical trials with 22 stroke patients were performed to exanimate the effectiveness of the propose VR system. Moreover, a variety of motor indicators derived via motion trajectory were proposed. Further, integrating multi-model data, such as motion trajectory, task performance and evaluation scales, machine-learning method was applied to develop evidence-based assessment models in order to evaluate upper-limb motor function. The results indicated that the proposed VR system was significantly effective for motor rehabilitation. Also, a few motor indicators were found significantly different between pre and post trials and were highly correlated with the evaluation scales. Finally, with the fusion of multi-model data, the accuracy rate of machine-learning assessment model was up to 92.72% which revealed its great potential for clinical use.