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A system of human vital signs monitoring and activity recognition based on body sensor network
Wang ZL(王哲龙); Zhao, Cong; Qiu, Sen
Department机器人学研究室
Source PublicationSensor Review
ISSN0260-2288
2014
Volume34Issue:1Pages:42-50
Indexed BySCI ; EI
EI Accession number20140417218744
WOS IDWOS:000330600500006
Contribution Rank1
KeywordBlood Pressure Body Sensor Networks Decision Trees Face Recognition Heart Motion Estimation Oxygen Patient Monitoring Sensor Nodes Telemedicine
AbstractPurpose - The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN). Design/methodology/approach - The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time. Findings - Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO2 and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC). Practical implications - The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future. Originality/value - First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO2, temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectInstruments & Instrumentation
WOS Research AreaInstruments & Instrumentation
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/14721
Collection机器人学研究室
Corresponding AuthorZhao, Cong
Affiliation1.Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Wang ZL,Zhao, Cong,Qiu, Sen. A system of human vital signs monitoring and activity recognition based on body sensor network[J]. Sensor Review,2014,34(1):42-50.
APA Wang ZL,Zhao, Cong,&Qiu, Sen.(2014).A system of human vital signs monitoring and activity recognition based on body sensor network.Sensor Review,34(1),42-50.
MLA Wang ZL,et al."A system of human vital signs monitoring and activity recognition based on body sensor network".Sensor Review 34.1(2014):42-50.
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