Abstract
Cloud computing has revolutionized healthcare in today's world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends.
Highlights
Activity recognition, remote monitoring and healthcare services provisioning are gaining more importance with active lifestyle of users and development of sophisticated technology
In Hadoop there are many traffic considerations as the nodes communicate a lot on the network during MapReduce jobs
In this paper a technique has been proposed for behavior life style analysis through activity recognition and large scale data mining in mobile sensory data through MapReduce
Summary
Remote monitoring and healthcare services provisioning are gaining more importance with active lifestyle of users and development of sophisticated technology. There are many challenges faced by the smartphones and mobile devices with regard to computation and energy consumption [2] Those limitations must be addressed when developing mobile applications. A further advantage of the concept is that mobile cloud applications are not limited to a certain device or operating system Based on this smartphones are considered as the mobile device within our work as they are connected to the internet through wireless and 3G and 4G networks. The current solutions are manual and slow due to lack of real time data collection which results in slow monitoring and diagnosis With this in mind a mobile application is needed which will be optimal in terms of energy and processing power and does not drain the battery time.
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