文献类型：期刊文献

中文题名：基于KF-LSTM模型的手写数字轨迹的sEMG重建算法

英文题名：Hybrid KF-LSTM Model for sEMG-Based Handwriting Numeral Traces Reconstruction

作者：杨钟亮[1];文杨靓[1];陈育苗[2]

机构：[1]东华大学机械工程学院,上海201620;[2]华东理工大学艺术设计与传媒学院,上海200237

年份：2019

卷号：31

期号：7

起止页码：1247

中文期刊名：计算机辅助设计与图形学学报

外文期刊名：Journal of Computer-Aided Design & Computer Graphics

收录：CSTPCD;;EI(收录号：20194207539139);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家自然科学基金(51305077);中央高校基本科研业务费专项资金(2232018D3-27);东华大学研究生创新创业能力训练计划(103-06-0041038);浙江省健康智慧厨房系统集成重点实验室开放基金(2014E10014);上海市设计学Ⅳ类高峰学科资助项目(DC17013);东华大学2017年研究生核心课程建设项目(201711)

语种：中文

中文关键词：手写重建;表面肌电;长短期记忆网络;卡尔曼滤波

外文关键词：handwriting reconstruction;surface electromyography (sEMG);long short-term memory network;Kalman filter

摘要：为了从神经肌肉活动中有效地重建出手写轨迹,提出一种卡尔曼滤波器与长短期记忆网络深度融合的混合模型(KF-LSTM),对手写数字轨迹坐标映射的表面肌电(sEMG)信号进行训练与解码.招募5名被试,设计了组间实验和组内实验方案,同步采集手写过程中的sEMG和轨迹坐标,构建基于KF-LSTM的手写轨迹预测模型;以决定系数和主观可辨认度作为评价指标,分别与LSTM模型、浅层神经网络(NN)模型以及KF模型的重建结果进行比较.实验结果表明,KF-LSTM模型在组间实验及组内实验中的表现均高于其他3种方法,能有效地提升重建精度,提高重建轨迹的光顺度。
For the purpose of reconstructing handwriting traces from neuromuscular activities effectively, a well-integrated Kalman filter modified long-short-term memory network hybrid method (KF-LSTM) is proposed, which can train and decode the sEMG (surface electromyography) signals to the corresponding coordinates of handwriting numeral traces. Five participants were recruited for the between-group and within-group experiments. After synchronously collecting the sEMG signals and coordinates in the handwriting process, the KF-LSTM pre- diction models were constructed. The decision coefficient and the subjective identifiability were calculated as the evaluation indices. The performance of the KF-LSTM models was compared with the LSTM models, the NN (neural network) models and the KF (Kalman filter) models. The experiment results show that the proposed KF-LSTM method perform better than the other 3 methods, improve the reconstruction accuracies and make re- constructed traces much smoother.