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17(01) 76-86
Integrated Solution for Physical Activity Monitor-ing Based on Mobile Phone and PC
Mi Hee Lee, MS1, Jungchae Kim MS1, Sun Ha Jee, PhD2, Sun Kook Yoo, PhD1,3
1Department of Medical Engineering, Yonsei University College of Medicine; 2Institute for Health Promotion, Yonsei University Graduate School of Public Health; 3Brain Korea 21 for the College of Medical Science, Yonsei University, Seoul, Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objectives: This study is part of the ongoing development of treatment methods for metabolic syndrome (MS) project, which involves monitoring daily physical activity. In this study, we have focused on detecting walking activity from subjects which includes many other physical activities such as standing, sitting, lying, walking, running, and falling. Specially, we implement-ed an integrated solution for various physical activities monitoring using a mobile phone and PC. Methods: We put the iPod touch has built in a tri-axial accelerometer on the waist of the subjects, and measured change in acceleration signal according to change in ambulatory movement and physical activities. First, we developed of programs that are aware of step counts, velocity of walking, energy consumptions, and metabolic equivalents based on iPod. Second, we have developed the activity recognition program based on PC. iPod synchronization with PC to transmit measured data using iPhoneBrowser program. Using the implemented system, we analyzed change in acceleration signal according to the change of six activity patterns. Results: We compared results of the step counting algorithm with different positions. The mean accuracy across these tests was 99.6 ± 0.61%, 99.1 ± 0.87% (right waist location, right pants pocket). Moreover, six activities recognition was performed using Fuzzy c means classification algorithm recognized over 98% accuracy. In addition we developed of programs that syn-chronization of data between PC and iPod for long-term physical activity monitoring. Conclusions: This study will provide evidence on using mobile phone and PC for monitoring various activities in everyday life. The next step in our system will be addition of a standard value of various physical activities in everyday life such as household duties and a health guideline how to select and plan exercise considering one's physical characteristics and condition.
Healthcare Informatics Research 2011 Mar; 17(01) 76-86
Keyword : Walking, Ambulatory Monitoring, Cellular Phone

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