可穿戴传感步态模式深度学习融合判别模型

Deep Learning Fusion Discrimination Model for Wearable Gait Patterns

为有效提高鉴别可穿戴传感数据步态模式的准确度,本文提出一种将卷积神经网络和长短时记忆神经网络相融合的深度学习步态模式判别新模型,该模型充分利用卷积神经网络所具获取最具数据局部空间特征和长短时记忆神经网络模型所具获取数据内在特征时间相关性优异特性,有效挖掘隐含于高维性、非线性、随机性可穿戴传感时序步态数据与步态模式变化密切相关的时-空步态特征,提高步态模式分类性能.采用美国加州大学UCI数据库HAR数据集评价本文所提模型的有效性,实验结果表明,本文所提模型可有效获取内嵌于可穿戴传感步态数据的时-空步态特征,分类准确率可达91.45%、精确率可达91.54%以及召回率可达91.53%,分类性能显著优于传统机器学习模型,为准确鉴别可穿戴传感数据步态模式提供一个新的解决方案.

In order to improve the accuracy of gait pattern identification for wearable sensor data,we propose a new model of deep learning-based gait pattern identification,which combines a convolutional neural network with a long short-term memory neural network in this study.This model makes full use of the local spatial features of data obtained by a convolution neural network and the excellent correlation of intrinsic feature time of data obtained by a long shortterm memory neural network model.It can effectively mine the temporal-spatial gait features implied by highdimensional,nonlinear,and random temporal gait data of wearable sensors which are closely related to gait pattern changes,improving the model’s classification performance of gait patterns.The UCI HAR data set from University of California is used to validate the proposed model.The experimental results show that the model can effectively collect temporal-spatial gait features embedded in the gait data of wearable sensors.This can reach classification accuracy of 91.45%,precision of 91.54%,and recall of 91.53%,signaling significantly better classification performance than that of traditional machine learning models,which serves as a new scheme for the accurate gait pattern identification of wearable sensor data.