HotMobile 2012 Poster: MachineSense: Detecting and Monitoring Active Machines using Smart Phone

Detecting and tracking individual running machine at home has variety of implication at context-aware application for home automation, energy monitoring, machine health monitoring, human activity detection, etc. Researchers have came up with various ideas of detecting machines or machine related events based on their interest of problems. Unfortunately, all these solutions require invasive and expensive installation of sensor devices. In order to address these problems, we propose a simple and flexible machine monitoring system using smart phones. We call our system MachineSense in which it exploits various sensors in smart phones to build a unique fingerprint profile for each individual machine. In building fingerprint profile, each machine’s characteristics are analyzed to identify the sensors ( such as magnetic sensor, light, microphone, temperature, camera, RF etc.) that could be utilized to collect sensing data. These sensing data in collective way represent a fingerprint profile of the machine. Later, we apply a machine learning method using fingerprint profile for recognizing running machine. In this poster, we refer to all kind of home appliances, computing machines and non-computing machines as ”machine”.