Human Activity Recognition Using Smartphone Sensors

Abstract

In today’s digitalized world, smartphones are the devices which have become a basic and fundamental part of our life. Since, these greatest technology’s appearance, an uprising has been created in the industry of mobile communication. These greatest inventions of mankind are not just constricted for calling these days. As the capabilities and the number of smartphone users increase day by day, smartphones are loaded with various types of sensors which captures each and every moment, activities of our daily life. Two of such sensors are Accelerometer and Gyroscope which measures the acceleration and angular velocity respectively. These could be used to identify the human activities performed. Basically, Human Activity Recognition is a classifying activity with so many use cases such as health care, medical, surveillance and anti-crime securities. Smartphones have wide variety of applications in various fields and can be used to excavate different kinds of data which provide accurate insights and knowledge about the user's lifestyle. Nowadays creating lifelogs that is a technology to capture and record a user's life through his or her mobile devices, are becoming very important task. An immense issue in creating a detailed lifelog is the accurate detection of activities performed by human based on the collected data from the sensors. The data in the lifelogs has strong association with physical health variables. These data are motivational and they identify any type of behavioral changes. These data provide us the overall measure of physical activity. In this project, we have analyzed the smartphone sensors produced data and used them to recognize the activities performed by the user.