摘要： 人体行为识别利用深度学习网络模型自动提取数据的深层特征，但传统机器学习算法存在依赖手工特征提取、模型泛化能力差等问题。提出基于空时特征融合的深度学习模型（CLT-net）用于人体行为识别。采用卷积神经网络（CNN）自动提取人体行为数据的深层次隐含特征，利用长短时记忆（LSTM）网络构建时间序列模型，学习人体行为特征在时间序列上的长期依赖关系。在此基础上，通过softmax分类器实现对不同人体行为分类。在DaLiAc数据集的实验结果表明，相比CNN、LSTM、BP模型，CLT-net模型对13种人体行为的总体识别率达到了97.6%，具有较优的人体行为识别分类性能。

关键词: 人体行为识别, 深度学习, 卷积神经网络, 长短时记忆网络, 模式识别, 可穿戴传感器

Abstract: Human activity recognition is a deep learning-based technology, which uses deep learning network models to automatically extract deep features of data.The traditional machine learning algorithms rely heavily on manual intervention during feature extraction, and exhibit a poor generalization ability.To address the problem, a deep learning model, CLT-net, is proposed based on space-time feature fusion for human activity recognition.CLT-net employs Convolution Neural Network (CNN) to extract the deep hidden features of human activity data automatically.Also, Long Short-Term Memory (LSTM) network is used to construct the time series model to learn the long-term dependence of human activity features on the time series.Finally, the softmax classifier is used to classify different human activities.The experimental results based on the public dataset, DaLiAc, show that CLT-net achieves an accuracy of 97.6% in the recognition of 13 kinds of human activities, outperforming the traditional models based on CNN, LSTM and BP.CLT-net has better classification performance of human activity recognition.

Key words: human activity recognition, Deep Learning(DL), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM) network, pattern recognition, wearable sensors

中图分类号: 

• TP391