A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.
A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.
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DOI：
10.3390/s23187917
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发表时间：
2023-09-15
期刊：
影响因子：
--
通讯作者：
Young SD
中科院分区：
文献类型：
--
作者：
Emish M;Kelani Z;Hassani M;Young SD
移动健康的日益普及为使用移动的电话应用程序（app）收集丰富的数据集提供了机会。我们的健康监测移动的应用程序使用运动检测来跟踪个人的身体活动和位置。收集的数据用于改善健康结果，例如通过分析身体活动模式降低慢性病的风险和促进更健康的生活方式。使用智能手机运动检测传感器和GPS接收器，我们实现了一种节能跟踪算法，可以在用户运动时捕获用户位置。为了确保数据收集和存储的安全性和效率，加密算法用于无服务器和可扩展的云存储设计。数据库架构是围绕移动的广告ID（MAID）设计的，作为每个设备的唯一标识符，允许准确跟踪和高数据质量。我们的应用程序使用谷歌的活动识别应用程序编程接口（API）在Android操作系统或地理围栏和运动传感器在iOS上跟踪大多数智能手机可用。此外，我们的应用程序利用区块链和传统支付来简化补偿，并具有直观的用户界面，以鼓励参与研究。这款移动的追踪应用在iPhone 14 Pro Max上进行了20天的测试，发现它在移动过程中准确捕捉位置，并在不活动时段后迅速恢复追踪，同时在后台运行时消耗的电池寿命百分比很低。
The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual’s physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google’s Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.
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影响因子：
4.5
作者：
Burgi, Rahel;Tomatis, Laura;de Bruin, Eling D.
通讯作者：
de Bruin, Eling D.
影响因子：
4.8
作者：
Chang, Ting;Hu, Yingjie;Quigley, Brian M.
通讯作者：
Quigley, Brian M.
DOI：
10.3390/s150818901
发表时间：
2015-07-31
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
del Rosario MB;Redmond SJ;Lovell NH
通讯作者：
Lovell NH
影响因子：
4.6
作者：
de Montjoye, Yves-Alexandre;Hidalgo, Cesar A.;Verleysen, Michel;Blondel, Vincent D.
通讯作者：
Blondel, Vincent D.
DOI：
10.1016/j.jag.2022.102752
发表时间：
2022-04
期刊：
International journal of applied earth observation and geoinformation : ITC journal
影响因子：
--
作者：
Garett RR;Yang J;Zhang Q;Young SD
通讯作者：
Young SD