Integrated Solution for Physical Activity Monitoring Based on Mobile Phone and PC
Integrated Solution for Physical Activity Monitoring Based on Mobile Phone and PC
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DOI：
10.4258/hir.2011.17.1.76
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发表时间：
2011-03-01
影响因子：
2.9
通讯作者：
Yoo, Sun Kook
中科院分区：
文献类型：
--
作者：
Lee, Mi Hee;Kim, Jungchae;Yoo, Sun Kook
目的：这项研究是正在进行的代谢综合征（MS）治疗方法开发项目的一部分，该项目涉及监测日常体力活动。在这项研究中，我们专注于检测受试者的行走活动，其中包括许多其他身体活动，如站立，坐着，躺下，行走，跑步和跌倒。特别是，我们使用移动的手机和PC实施了用于各种身体活动监测的集成解决方案。研究方法：我们将iPod touch内置的三轴加速度计置于受试者的腰部，并根据步行运动和身体活动的变化测量加速度信号的变化。首先，我们开发的程序，知道步数，步行速度，能量消耗和代谢当量的基础上的iPod。第二，开发了基于PC机的活动识别程序。iPod与PC同步，使用iPhoneBrowser程序传输测量数据。使用所实现的系统，我们分析了加速度信号的变化，根据六个活动模式的变化。结果：比较了不同位置的步数算法的结果。这些测试的平均准确度为99.6 +/-0.61%，99.1 +/- 0.87%（右腰位置，右裤兜）。此外，六个活动的识别，使用模糊c均值分类算法识别超过98%的准确率。此外，我们还开发了PC和iPod之间的数据同步程序，用于长期的身体活动监测。结论：本研究将为使用移动的手机和PC监测日常生活中的各种活动提供证据。我们的系统的下一步将是增加日常生活中各种身体活动的标准值，如家务劳动，以及如何根据个人的身体特征和状况选择和计划运动的健康指南。
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 implemented 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 synchronization 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.