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Design, implementation and validation of a novel open framework for agile development of mobile health applications.

Banos O, Villalonga C, Garcia R, Saez A, Damas M, Holgado-Terriza JA, Lee S, Pomares H, Rojas I - Biomed Eng Online (2015)

Bottom Line: The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines.An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid.This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
The delivery of healthcare services has experienced tremendous changes during the last years. Mobile health or mHealth is a key engine of advance in the forefront of this revolution. Although there exists a growing development of mobile health applications, there is a lack of tools specifically devised for their implementation. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps. The framework is particularly planned to leverage the potential of mobile devices such as smartphones or tablets, wearable sensors and portable biomedical systems. These devices are increasingly used for the monitoring and delivery of personal health care and wellbeing. The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines. An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid. This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth. An accurate activity recognition model is developed and successfully validated in both offline and online conditions.

No MeSH data available.


Related in: MedlinePlus

Examples of representation modes supported by mHealthApp. (Left) Tri- axial acceleration signals are represented at runtime. (Right) Monthly average heart rate data is depicted on the top, while continuous 2-leads ECG signals are plotted at the bottom.
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Figure 2: Examples of representation modes supported by mHealthApp. (Left) Tri- axial acceleration signals are represented at runtime. (Right) Monthly average heart rate data is depicted on the top, while continuous 2-leads ECG signals are plotted at the bottom.

Mentions: The information collected by the wearable or mobile sensors can be depicted through the visualization menu. mHealthApp supports diverse representation modes and plot types. An online representation of the signals is provided to visualize the data collected at runtime, e.g., registered motion data (Figure 2 left and bottom-right). This type of representation is particularly useful for specialists to analyze vital sign patterns. Moreover, average values or general statistics of processed data can be also represented, e.g., heart rate averaged by month (Figure 2 top-right). This kind of graphic is specially devised for the average user, although it is also practical for the expert user.


Design, implementation and validation of a novel open framework for agile development of mobile health applications.

Banos O, Villalonga C, Garcia R, Saez A, Damas M, Holgado-Terriza JA, Lee S, Pomares H, Rojas I - Biomed Eng Online (2015)

Examples of representation modes supported by mHealthApp. (Left) Tri- axial acceleration signals are represented at runtime. (Right) Monthly average heart rate data is depicted on the top, while continuous 2-leads ECG signals are plotted at the bottom.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4547155&req=5

Figure 2: Examples of representation modes supported by mHealthApp. (Left) Tri- axial acceleration signals are represented at runtime. (Right) Monthly average heart rate data is depicted on the top, while continuous 2-leads ECG signals are plotted at the bottom.
Mentions: The information collected by the wearable or mobile sensors can be depicted through the visualization menu. mHealthApp supports diverse representation modes and plot types. An online representation of the signals is provided to visualize the data collected at runtime, e.g., registered motion data (Figure 2 left and bottom-right). This type of representation is particularly useful for specialists to analyze vital sign patterns. Moreover, average values or general statistics of processed data can be also represented, e.g., heart rate averaged by month (Figure 2 top-right). This kind of graphic is specially devised for the average user, although it is also practical for the expert user.

Bottom Line: The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines.An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid.This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
The delivery of healthcare services has experienced tremendous changes during the last years. Mobile health or mHealth is a key engine of advance in the forefront of this revolution. Although there exists a growing development of mobile health applications, there is a lack of tools specifically devised for their implementation. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps. The framework is particularly planned to leverage the potential of mobile devices such as smartphones or tablets, wearable sensors and portable biomedical systems. These devices are increasingly used for the monitoring and delivery of personal health care and wellbeing. The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines. An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid. This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth. An accurate activity recognition model is developed and successfully validated in both offline and online conditions.

No MeSH data available.


Related in: MedlinePlus