东华大学学报（英文版）2024，Vol.41Issue(3)：231-240,10.DOI:10.19884/j.1672-5220.202309005
用于位置信息辅助复杂人体行为识别的新型深度学习框架
A Novel Deep Learning Framework for Location Information Assisted Complex Human Activity Recognition
摘要
Abstract
With the popularization of smart living and the rapid development of wearable terminal technology in recent years,sensor-based human activity recognition(HAR)has attracted widespread attention and has significant academic research and commercial application value.This paper focuses on enhancing the HAR model's recognition of users'daily simple activities(SAs)and complex activities(CAs),and proposes a deep learning(DL)model.Firstly,two publicly available datasets,UCI HAR and Shoaib CHA,are normalized.Then the characteristics of distinct activities are retrieved by the proposed model for HAR.Given the high association between users'activities and locations,location information is integrated into the dataset by the one-hot encoding technique to boost the model's classification performance.In addition,the proposed DL model is evaluated against eight traditional machine learning(ML)algorithms and six DL algorithms.Finally,the effect of various types of activities on the HAR model's recognition ability is studied.The experimental findings reveal that the proposed model achieves the highest classification accuracy on UCI HAR and Shoaib CHA datasets,with 96.77％and 99.13％,respectively.The classification accuracy of the HAR model is also greatly enhanced for both SAs and CAs by adding location information to the datasets.关键词
人体行为识别(HAR)/机器学习(ML)/深度学习(DL)/可穿戴传感器/卷积神经网络/长短期记忆(LSTM)神经网络Key words
human activity recognition(HAR)/machine learning(ML)/deep learning(DL)/wearable sensor/convolutional neural network/long short-term memory(LSTM)neural network分类
信息技术与安全科学引用本文复制引用
于静伟,张磊,高震宇,倪琴..用于位置信息辅助复杂人体行为识别的新型深度学习框架[J].东华大学学报（英文版）,2024,41(3):231-240,10.基金项目
National Natural Science Foundation of China(Nos.62371118,6210020445 and 61901104) （Nos.62371118,6210020445 and 61901104）
Natural Science Foundation of Shanghai,China(Nos.21ZR1446900 and 21511100102) （Nos.21ZR1446900 and 21511100102）
Science and Technology Research Project of Shanghai Songjiang District,China(No.20SJKJGG4C) （No.20SJKJGG4C）
