[1]李文娟,沈 澍,孙绍山,等.智能设备上步态识别系统设计与实现[J].计算机技术与发展,2022,32(12):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 009]
　LI Wen-juan,SHEN Shu,SUN Shao-shan,et al.Design and Realization of Gait Recognition System in Intelligent Equipment[J].,2022,32(12):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 009]
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智能设备上步态识别系统设计与实现()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年12期
页码:
57-62
栏目:
媒体计算
出版日期:
2022-12-10

文章信息/Info

Title:
Design and Realization of Gait Recognition System in Intelligent Equipment
文章编号:
1673-629X(2022)12-0057-06
作者:
李文娟1 ; 沈 澍1; 2 ; 孙绍山1 ; 陈伟男1
1. 南京邮电大学 计算机学院,江苏 南京 210023;
2. 江苏省无线传感网高技术重点实验室,江苏 南京 210023
Author(s):
LI Wen-juan1 ; SHEN Shu1; 2 ; SUN Shao-shan1 ; CHEN Wei-nan1
1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing, 210023,China;
2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210023,China
关键词:
步态分析; 智能手机; 数据采集; 身份识别; 深度神经网络
Keywords:
gait analysis; smart phones; data collection; identity recognition; deep neural networks
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 12. 009
摘要:
步态是一种新兴的生物识别特征,将步态识别应用到实际生活中意义重大。 通过对人体步态进行分析,设计了一款身份识别系统,利用智能手机内置传感器采集人体步态信号并进行数据预处理,再通过部署于手机上的模型对步态数据进行识别。 采用智能手机作为搭载工具,实际应用成本低、能在更多复杂环境下进行操作,未来步态识别系统也可以拓展到更多可穿戴设备上。 系统主要包括了注册与识别两大模块。 注册主要是用户填写信息,系统建立用户个人的信息表用于存储相关数据。 注册还需要采集用户的步态数据,再将数据本地存储并上传至服务器,以用于后期的模型训练。 识别模块中,通过搭建的深度神经网络模型对预处理之后的数据进行自动特征提取与身份识别。 实验结果表明,所提出的神经网络模型在世界上最大的步态数据集 OU-ISIR(745 名受试者) 上的识别准确率达到了 82. 51% ,在实验采集的 30 名受试者的数据集中,该模型在任意位置下的识别准确率均达到了 92. 34% 以上。
Abstract:
Gait is a new biometrics feature. It is of great significance to apply gait recognition to real life. Based on the analysis of humangait,an identity recognition system is designed,which uses the inertial sensors of smart phone to collect human gait signal and preprocessthe data,and then identifies the gait data through the model deployed on the mobile phone. With smart phones as the carrying tool,theactual application cost is low and it can operate in more complex environments. In the future,the gait recognition system can also beextended to more wearable devices. The system mainly includes two modules:registration and recognition. Registration is mainly forusers to fill in the information,the system establishes the user’s personal information table for storage of relevant data. The registrationalso needs to collect the user ’s gait data, and then stores the data locally and upload it to the server for later model training. In therecognition module, the deep neural network model is built to automatically extract features and identify the pre - processed data.Experimental results show that the proposed neural network model has a recognition accuracy of 82. 51% on OU-ISIR,the largest gaitdata set in the world with 745 subjects,and a recognition accuracy of more than 92. 34% at any position in 30 subjects.

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更新日期/Last Update: 2022-12-10