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Integrating kinematic modeling and kinetics to quantify hand motor performance in persons with stroke

机译:结合运动学模型和动力学来量化中风患者的手部运动表现

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Objective: This study describes the design and testing of a system designed to quantify improvement in reaching and grasping abilities of the hemiparetic arm and hand of patients post stroke after a series of interactive virtual reality (VR) simulated training sessions. Methods: Five subjects post stroke participated in a two-week training session consisting of interactive computer games for 2–2.5 hours per session. Subjects'' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove™ and arm joint angles were measured using the trackSTAR™ system prior to training and after training. Downward force applied to the object during grasping was measured using a force sensor system. Results: There was a significant decrease in force applied to objects when grasping post VR training and an improvement in preshaping the hand during the transport phase. Conclusion: A system utilizing magnetic trackers, a data glove, and a force sensor may be sensitive to changes in motor performance elicited by a robotically facilitated, virtually simulated motor intervention.
机译:目的:本研究描述了一种系统的设计和测试,该系统旨在量化一系列交互式虚拟现实(VR)模拟训练课程后中风后患者偏瘫臂和手的伸手和抓握能力的提高。方法:五名中风后受试者参加了为期两周的培训课程,包括互动计算机游戏,每节课2–2.5小时。在进行运动之前,使用Cyber​​Glove™测量受试者的手指关节角度,以使用Cyber​​Glove™进行抓握测试;在训练之前和之后,使用trackSTAR™系统测量手臂的关节角度。使用力传感器系统测量在抓握过程中施加在物体上的向下力。结果:在进行VR训练后,施加在物体上的力显着降低,并且在运输阶段对手的塑形有所改善。结论:利用磁跟踪器,数据手套和力传感器的系统可能对由机器人辅助的,虚拟模拟的电机干预引起的电机性能变化敏感。

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