基于ARAT与视触融合的E手套康复评估与训练系统

E Glove Evaluation & Training System Based on ARAT and Fusion of Visual and Tactile Information

针对目前用于手功能评估系统的可穿戴式传感数据手套装置结构复杂、灵活性和实用性低下问题,该文为激发患者的主动运动功能,提出了一种基于视触融合的可穿戴式压力传感数据手套装置(EGET系统)。首先,介绍了用于评估手功能的上肢动作研究测试理论,其基本的测试流程及评分准则;其次,分别描述了视觉信息和触觉信息的处理过程,以及视触融合的手功能评估测试方法,实现对患者训练过程的数字化记分和评估;最后,选择10位脑卒中患者使用EGET系统进行手功能测试与评估,并与医生评分进行对比分析,采用EGET系统自动评分相对医生评分的最大相对误差率为8%,平均相对误差率为4%,说明EGET系统能够实现预期的目标。

Aiming to solve the problem of complex structure, low flexibility and practicability for a wearable sensing data glove device of hand function evaluation system, this paper presented a wearable pressure-sensing data glove device based on visual and tactile fusion, which can stimulate the active motor function of patients. Firstly, it introduced the upper limb action research test theory, the basic test flow and the grading rules that used to evaluate the hand function. Secondly, it described the processing flow of visual and tactile information, and the hand function evaluation test method of visual and tactile fusion, which was used to achieve digital score and evaluation of the patient training process. Finally, ten patients with stroke were enrolled into the EGET system for hand function test and evaluation. The results were compared with the doctors＇. The maximum relative error is 8%, and the average relative error is 4%. which means that EGET system can achieve the expected goal.