Human Action Recognition Using Smartphone Sensors

As smartphones are becoming ubiquitous, many studies using smartphones are being investigated in recent years. Further, these smartphones are being laden with several diverse and sophisticated sensors like GPS sensor, vision sensor (camera), acceleration sensor, audio sensor (microphone), light sensor, and direction sensor (compass). Activity Recognition is one of the potent research topics, which can be used to provide effective and adaptive services to users. Our paper is intended to evaluate a system using smartphone-based sensors used for acceleration, referred to as an accelerometer. To understand six different human activities using supervised machine learning classification; to execute the model a compiled accelerometer data from different sixteen users are collected as per their usual day to day routine consisting of sitting, standing, laying down, walking, climbing up and down the staircase. The sample data thus generated then have been aggregated and combined into examples upon which supervised machine learning algorithms have been applied to generate predictive models. To address the limitations of laboratory settings, we have used the Physics Toolbox Sensor Suite with the Google Android platform to collect these timeseries data generated by the smartphone accelerometer. This kind of activity prediction model can be used to provide insightful information about millions of human beings merely by making them contain a smartphone with them.