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Behavior life style analysis for mobile sensory data in cloud computing through MapReduce.

Shujaat Hussain1, Jae Hun Bang2, Manhyung Han3

  • 1Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea. shujaat.hussain@oslab.khu.ac.kr.

Sensors (Basel, Switzerland)
|November 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid cloud system for mobile health applications to collect and analyze sensory data. It uses big data technologies to reveal user activity trends and health insights.

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Area of Science:

  • Health Informatics
  • Mobile Computing
  • Big Data Analytics

Background:

  • Cloud computing integration in healthcare enables mobile and sensor data collection.
  • Existing systems face challenges in processing and analyzing large volumes of heterogeneous health data.

Purpose of the Study:

  • To propose a hybrid and distributed cloud environment for mobile health data.
  • To develop an activity recognition application for data collection and cloud storage.
  • To leverage big data analytics for health insights and trend identification.

Main Methods:

  • Developed a mobile application for activity recognition and data collection.
  • Implemented a hybrid cloud infrastructure for secure data storage.
  • Utilized Hadoop MapReduce for processing and analyzing large-scale sensory data.
  • Visualized user activity timelines to identify health trends.

Main Results:

  • Successfully collected and stored sensory data from mobile devices in the cloud.
  • Generated user activity timelines and performed health analytics.
  • Demonstrated the capability of the big data solution in uncovering lifestyle trends.

Conclusions:

  • The proposed big data solution effectively analyzes mobile health data for actionable insights.
  • Hybrid cloud environments are suitable for scalable and efficient healthcare data processing.
  • This approach offers a novel way to understand user behavior and promote healthier lifestyles.