面向手部康复应用的数据手套技术研究

疾病或意外发生后经常会引起患者的手部功能障碍,但目前手部康复评估仍然依赖医生经验等主观判断,市场上现有的部分康复产品结构复杂、价格昂贵,而且无法长期记录病人的康复数据,不便于医生进行诊断和复查。提出了一种面向手部康复的多传感器数据融合手套,采用最小二乘法对加速度和地磁进行校正,利用互补滤波进行传感器数据融合,依据真实手骨模型进行虚拟手部建模,用数据手套获得的手部姿态驱动Unity3D中的手部模型。该系统可以准确地记录手的姿态信息,并在虚拟环境中进行实时还原,能高效地帮助医生进行康复评估和协助患者进行康复训练。

Research on data glove for hand rehabilitation

Diseases or accidents often lead to hand dysfunction. At present, the assessment of hand rehabilitation mainly depends on the experience and judgment of doctors. Most of hand rehabilitation products on the market are complex and expensive. The daily training data for further diagnosis of patients cannot be recorded by the existed products. In this paper, a data glove with multi sensors is designed for hand rehabilitation. The least square approach is adopted to correct the accelerometer and magnetometer. The complementary filter algorithm is used for sensor data fusion. Real time hand gesture can be obtained and demonstrated in Unity 3D platform. Four kinds of gestures are captured and reconstructed by the proposed approach. It reveals that the data glove can effectively improve the hand rehabilitation procedure for both patients and doctors.