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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真 | |
| dc.contributor.author | Sio-Fong Hoi | en |
| dc.contributor.author | 許少鋒 | zh_TW |
| dc.date.accessioned | 2021-06-16T13:41:01Z | - |
| dc.date.available | 2015-07-19 | |
| dc.date.copyright | 2013-07-19 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-12 | |
| dc.identifier.citation | [1] A. Anjum and M. Ilyas. Activity recognition using smartphone sensors. In Con- sumer Communications and Networking Conference (CCNC), 2013 IEEE, pages 914– 919, 2013.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62322 | - |
| dc.description.abstract | In order to help people to maintain a healthy life style in today’s busy world, hav- ing a device that can accurately and conveniently calculate calories consumption will be crucial. This research paper has found a reliable system that can accurately report a user’s calories consumption rate by using the accelerometer, gyroscope and GPS sen- sors in mobile phones. It has successfully overcome the orientation problem that trou- bled many previous scholars. Resolving this issue is critical because mobile phones can be placed anywhere by users in real life.
Firstly, this system can accurately recognize the following six most common phys- ical activities of a user’s walking, running, cycling, going upstairs, going downstairs and idling (stationary). By computing the amplitude of 1) vertical components, 2) magnitude of horizontal components, 3) vibration of the vertical and horizontal com- ponents, and 4) angle of the vertical and horizontal components, this system can attain an average accuracy rate of 90.44%, better than 73.09% in Yang’s method. This high accuracy rate is significant because wrong recognition of activities will amplify the margin of error in calories consumption rate calculation. Secondly, this system will use the ACSM Metabolic Equations, published by Amer- ican College of Sports Medicine and approved by many scholars, to calculate calories consumption. By applying the results generated from the above method to these equa- tions, this system can determine a user’s calories consumption rate with a mere margin of mean absolute error of 11.45%. It is significantly better than the consumption rate generated with Yang’s method, which has a margin of mean absolute error of 24.97%. Together, this system can accurately recognize a user’s activity independent of orien- tation, which is significant in estimating accurately the calories consumption rate. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T13:41:01Z (GMT). No. of bitstreams: 1 ntu-102-R00922145-1.pdf: 1380942 bytes, checksum: feb338d21c5acb2e281608be460dc595 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | Abstract iii
List of Figures viii List of Tables xii Chapter 1 Introduction 1 1.1 Motivation................................ 2 1.1.1 Physical Activity Recognition ................. 2 1.1.2 Calories Consumption ..................... 3 1.2 Research Objectives........................... 3 1.3 Challenges................................ 4 1.3.1 The Influence of Realistic Conditions . . . . . . . . 4 1.3.2 The Model of Activity Recognition . . . . . . . . . 4 1.3.3 Orientation Problem ...................... 4 1.3.4 The Most Common Activities Problem . . . . . . . 5 1.4 Thesis Organization........................... 5 Chapter 2 Related Works 7 2.1 Physical Activity Recognition ..................... 7 2.1.1 Different or multiple sensors on different position of different parts of the body ........................ 7 2.1.2 Hardware combining multiple types of sensors . . . . . . . . 8 2.1.3 Only one accelerometer not in mobile phone . . . . . . . . . 9 2.1.4 Mobile Sensor-Based Method ................. 10 2.2 Calories Burnt Estimation........................ 14 Chapter 3 Physical Activity Level and Calories Consumption Estimation 15 3.1 Assumption............................... 16 3.2 Proposed Solution............................ 16 3.2.1 Physical Activity Classification Problem . . . . . . . . . . . . 16 3.2.2 Input Data Preprocessing.................... 37 3.2.3 Calories Consumption Estimation ............... 38 Chapter 4 Implementation 43 4.1 Hardware Setup............................. 43 4.2 Software Setup ............................. 44 4.2.1 Data Collection......................... 44 4.2.2 Data Analysis.......................... 46 Chapter 5 Experiments and Evaluations 49 5.1 Data Collection ............................. 49 5.1.1 Data Description ........................ 50 5.1.2 Data Observation........................ 52 5.1.3 Evaluation ........................... 55 5.1.4 Model Improvement ...................... 65 Chapter 6 Conclusion 69 6.1 Summary of Contributions ....................... 71 6.2 Limitation................................ 71 6.2.1 Sensor data calibration..................... 71 6.2.2 The variable of METs equations................ 72 6.3 Future Works .............................. 72 Bibliography 74 | |
| dc.language.iso | en | |
| dc.subject | 活動識別 | zh_TW |
| dc.subject | 人常生活 | zh_TW |
| dc.subject | 卡路里 | zh_TW |
| dc.subject | daily life | en |
| dc.subject | physical activity recognition | en |
| dc.subject | calories estimate | en |
| dc.title | 以行動智慧裝置進行活動辨識與熱量消耗之研究 | zh_TW |
| dc.title | Physical Activity Recognition and Calories Consumption Estimation by Mobile Phones | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳彥仰,蔡宗翰,黃漢申,朱浩華 | |
| dc.subject.keyword | 活動識別,卡路里,人常生活, | zh_TW |
| dc.subject.keyword | physical activity recognition,calories estimate,daily life, | en |
| dc.relation.page | 78 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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