Cell Phone based Activity Detection using Markov Logic Network

Mobile devices are becoming increasingly sophisticated and the latest generation of smart phones now incorporates many diverse and powerful sensors, like GPS sensors, light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). In this project we are trying to build a system that uses phone-based accelerometers, gyroscope and magnetometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. In order to address the activity recognition task as a sequential supervised learning problem, we are going to represnt the collected sensor data a time series and we will use Markov Logic Network to model this time series data.