A Low-Cost Telerehabilitation Paradigm for Bimanual Training
A Low-Cost Telerehabilitation Paradigm for Bimanual Training
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DOI：
10.1109/tmech.2021.3064930
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发表时间：
2022-02-01
影响因子：
6.4
通讯作者：
Porfiri, Maurizio
中科院分区：
文献类型：
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作者：
Barak-Ventura, Roni;Ruiz-Marin, Manuel;Porfiri, Maurizio
COVID-19 大流行改变了人们的日常生活，因为人们必须减少彼此之间的接触以防止疾病传播。因此，患者获得门诊康复护理的机会受到限制，康复前景也受到影响。远程康复有可能为这些患者提供在家中同样有效的治疗。使用带有嵌入式运动传感器的商业游戏设备，可以收集运动数据，以客观评估运动表现，然后进行培训和记录进度。在此，我们提出了一种专用于双手运动的低成本远程康复系统，其中健康手臂驱动受影响手臂的运动。在建议的设置中，患者操纵 Microsoft Kinect 传感器前面嵌入传感器的销钉。为了提供一个有吸引力的锻炼环境，销钉与个人计算机连接，作为控制器。在定制的公民科学项目中，患者的手势被转化为行动。除了该系统之外，我们还引入了一种用于双手运动分类的算法，其内部工作原理根据降维、特征提取和运动分类执行的程序进行了详细说明。我们在八个健康受试者身上证明了我们的系统的可行性，为算法的有效性提供了支持。这些初步研究结果提出了在经济实惠的家庭康复中精确运动分析算法的发展。
The COVID-19 pandemic has transformed daily life, as individuals must reduce contacts among each other to prevent the spread of the disease. Consequently, patients' access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient's gestures are translated into actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation.