Human Activity Recognition to Estimate Burning Calories as Health Data using Features Extraction of Accelerometer Sensor Data
- Abstract
- Today the use of mobile phones not only for communication media but also as an entertainment, education, and even a health media. In this paper we focus on utilizing mobile phones as a health media by utilizing mobile phone sensors such as accelerometer or motion sensor to detect common user activities like lying, sitting, standing, walking, jogging and running. It can be recognized by extract the feature of accelerometer data because every activity has its own characteristics. Using these activities data we can obtain health data by estimate the number of burned calories.
The estimation of burned calories is obtained from the activity data conversion using Harris Benedict equation. We also calculate user burned calorie needs based on body condition like gender, age, weight, and high. It calculated using body mass index formula. Using both of these data, the mobile phone can know the progress of user to live healthily.
- Issued Date
- 2016
- Awarded Date
- 2016. 2
- Type
- Dissertation
- Publisher
- Pukyong National University, Interdisciplinary Program of Information System The Graduate School
- Affiliation
- Pukyong National University, Interdisciplinary Program of Information System The Graduate School
- Department
- 대학원 정보시스템협동과정
- Advisor
- Prof. Chang Soo Kim
- Table Of Contents
- Contents i
List of Tables iv
List of Figures v
Abstract vii
Chapter 1 1
Introduction 1
1.1. Background 1
1.2. Problem Statement 2
1.3. Thesis Objective 3
1.4. Scope 4
1.5. Thesis Outline 4
Chapter 2 6
Literature Reviews 6
2.1. Health 6
2.2. Obesity 6
2.3. Physical Activity 7
2.4. Body Mass Index (BMI) 8
2.5. Gamification 9
2.6. Pervasive Health Monitoring 10
2.7. Electronic Health Record 10
2.8. Physical Activity and Metabolic Equivalent of Task 11
2.9. Resting Metabolic Rate and Basal Metabolic Rate 12
2.10. Activity levels and MET 12
2.11. Energy expenditure or Calories Burned 13
2.12. Accelerometer 14
2.13. Treadmill 15
2.14. Pedometer 15
2.15. Mobile Phone Device 16
2.16. Android Platform 17
Chapter 3 18
System Requirements and Design 18
3.1. Requirement Analysis 18
3.1.1 User Requirement Analysis 18
3.1.2 System Requirement Analysis 19
3.2. System Specification 20
3.2.1 Software Requirement 20
3.2.2 Hardware 20
3.3 System Architecture 21
3.3.1 Architecture Design Process 21
3.4 Database Design 23
3.5 Block Diagram 24
3.5.1 Collecting Data from Sensor 24
3.5.2 Noise Reduction 26
3.5.3 Feature Extraction 28
3.5.4 Recognize Activity 31
3.5.5 Estimate Calorie Burn 31
Chapter 4 33
System Implementation, Testing, and Analysis 33
4.1. Implementation 33
4.2. Testing 33
4.2.1 Passive activities recognition 33
4.2.2 Accuracy of Active activities recognition 35
4.2.3 Burned Calories Estimation Accuracy of Passive Activities 36
4.2.4 Burned Calories Estimation Accuracy of Active Activities 37
4.3 Analysis 37
Chapter 5 40
Conclusions and Future Works 40
5.1. Conclusion 40
5.2. Future Works 40
References 42
Acknowledgement 43
- Degree
- Master
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