Mobile Sensor Data Classification for Human Activity Recognition using MapReduce on Cloud

Mobiles are equipped with different sensors like accelerometer, magnetic eld, and air pressure meter, which help in the process of extracting context of the user like location, situation etc. However, processing the extracted sensor data is generally a resource intensive task, which can be offloaded to the public cloud from mobiles. This paper specically targets at extracting useful information from the accelerometer sensor data. The paper proposes the utilization of parallel computing using MapReduce on the cloud for training and recognizing human activities based on classiers that can easily scale in performance and accuracy. The sensor data is extracted from the mobile, offloaded to the cloud and processed using three different classication algorithms, Iterative Dichotomizer 3, Naive Bayes Classier and K-Nearest-Neighbors. The MapReduce based algorithms are mentioned in detail along with one of their performance on Amazon cloud. The recognized activities can be used in mobile applications like our Zompopo that utilizes the information in creating an intelligent calendar