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研究生:Chinmayee Rayguru
研究生(外文):Chinmayee Rayguru
論文名稱:一個適用於物聯網健康監控及雲端異常偵測機制
論文名稱(外文):An IoT Based Fitness Monitoring and Cloud Aided Anomaly Detection Mechanism
指導教授:吳世琳吳世琳引用關係
指導教授(外文):S. L. Wu
學位類別:碩士
校院名稱:長庚大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:52
中文關鍵詞:物聯網健康監控異常偵測
外文關鍵詞:Internet Of ThingsFitness MonitoringAnomaly Detection
相關次數:
  • 被引用被引用:0
  • 點閱點閱:274
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  • 下載下載:23
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Recently, advancement in Internet of Things (IoT), smart devices and ubiquitous
healthcare systems improve the physical fitness level of a person. Due to unhealthy
life-style, Fitness Monitoring is a challenging issue to monitor the fitness level of a
person. In this thesis, we propose an IoT based fitness monitoring framework to alert
the people during unsafe fitness level. Besides, an anomaly detection mechanism is
introduced to identify the risk associated with the physiological parameters collected
using IoT devices. A cloud environment is used as the storage and analytic platform
in our proposed framework. Experimental results show that our proposed framework
can monitor the fitness parameters based on their high correlation values i.e. 63%
between heart rate and blood pressure with respect to age.
Chang Gung University Recommendation Letter from the Thesis Advisor
Chang Gung University Thesis/Dissertation Oral Defense Committee Certication
Acknowledgements iii
Abstract iv
Contents v
List of Figures viii
List of Tables ix
1 Introduction 1
1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Works 5
3 Proposed Framework 9
4 Fitness Data Collection and Preprocessing 13
4.1 Fitness Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 Blood Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1.2 Heart Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1.3 Pulse Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.4 Body Temperature . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.5 Body Mass Index . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Fitness Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Fitness Data Storage and Analysis in Cloud 21
5.1 Fitness Data Storage in Cloud . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Fitness Data Analysis in Cloud . . . . . . . . . . . . . . . . . . . . . 22
6 Simulations 27
6.1 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.1.1 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . 30
6.1.2 Fitness Level and Anomaly Detection . . . . . . . . . . . . . . 31
6.1.3 Anomaly Detection Accuracy . . . . . . . . . . . . . . . . . . 32
6.1.4 Sensitivity, Specificity and Accuracy . . . . . . . . . . . . . . 33
6.1.5 Processing time of proposed algorithm . . . . . . . . . . . . . 34
7 Conclusions 36
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Bibliography 37


List of Figures
3.1 Physical Fitness Monitoring Framework. . . . . . . . . . . . . . . . . 10
4.1 Collected Fitness Data. . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Data Collection through IoT Devices. . . . . . . . . . . . . . . . . . . 15
4.3 Blood Pressure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.4 Heart Rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5 Body Temperature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.1 Correlations among Fitness Monitoring Parameters. . . . . . . . . . . 23
6.1 Correlation analysis of fitness monitoring parameters. . . . . . . . . . 30
6.2 Fitness level of a user. . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.3 Accuracy of proposed algorithm. . . . . . . . . . . . . . . . . . . . . . 32
6.4 Fitness data for testing. . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.5 Processing time of proposed algorithm. . . . . . . . . . . . . . . . . . 34

List of Tables
2.1 Comparison and Contributions . . . . . . . . . . . . . . . . . . . . . . 8
4.1 Fitness Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.1 Normal and Abnormal Range of Fitness Parameters . . . . . . . . . . 24
5.2 Algorithm 1: Fitness Monitoring and Anomaly Detection . . . . . . . 26
6.1 Parameter list of data centers . . . . . . . . . . . . . . . . . . . . . . 28
6.2 Parameter List of Virtual Machines . . . . . . . . . . . . . . . . . . . 29
6.3 Parameter List of Cloudlets . . . . . . . . . . . . . . . . . . . . . . . 29

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