中文摘要：

在利用光学运动捕捉技术获取手部精细运动数据及手势信息的过程中,捕捉运动数据的缺失会对神经解码的性能产生影响,为此,提出一种基于主成分分析的缺失运动数据恢复和重建方法.该方法采用期望最大化算法在主成分空间和原始数据空间进行迭代映射,求解对应主成分空间,以提高原始空间数据修复的精度.实验分别从缺损数据长度、缺损数据维度、周期性运动数据及冗余数据等方面对该算法进行了验证,并与三次样条插值和一次迭代插值的结果进行了比较.对测试数据的实验结果表明：该方法适用于连续缺损数据长度小于350帧,或同时缺损数据维度小于13维的情况.手部运动的周期性规律对于提高数据恢复的精度有很大的帮助,冗余标记点也能在一定程度上减少数据恢复的结果误差.与三次样条插值和一次迭代插值方法相比,该方法的平均误差均小于10mm,仅相当于前两种方法误差的50%,甚至更少.

英文摘要：

Using optical motion capture system for acquiring accurate hand movements data and gesture information can be effective for neural decoding and related applications.However,data missing often occurs and affects the accuracy and efficiency of neural decoding results.A method based on principal component analysis was proposed for missing data recovery and rebuild.This method takes benefit of expectation maximization（EM）-like algorithm to increase the accuracy of recovery data results by mapping between original data space and principal component space iteratively for principal component space construction and refinement.To evaluate this method,four different experiments were involved,namely,missing frame length,missing dimension number,cyclic feature and redundant data.Recovered data results were compared with those of the cubic spline interpolation and one iteration interpolation methods.The experimental results show that,this method is applicable to missing frame length less than 350,missing dimension number smaller than 13.The cyclic feature of hand movements contributes to the increased accuracy of recovered data,and the redundant data is also helpful in reducing recovery data errors.When comparing the data results with those of the cubic interpolation and one iteration interpolation methods,the average error of this method was less than 10 mm,only 50% or less of the other two.