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A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer

A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer. Mi Zhang and Alexander A. Sawchuk Department of Electrical Engineering University of Southern California mizhang@usc.edu UbiMI workshop at ACM Ubicomp Conference, September 8, 2012.

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A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer

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  1. A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer Mi Zhang and Alexander A. Sawchuk Department of Electrical Engineering University of Southern California mizhang@usc.edu UbiMI workshop at ACM Ubicomp Conference, September 8, 2012

  2. Introduction • Background • Human activity recognition is one of the most basic problems in Ubiquitous Computing. • Applications: surveillance, security, health care, etc. • In this work, we focus on recognizing household activities by detecting appliance usage. • Existing Technology Computer Vision RFID Smart Meter Courtesy to Opportunity Project Courtesy to Intel Research Courtesy to IBM

  3. Magnetic Field Sensing • Idea • When household appliance is in operation, it generates electromagnetic waves. • The electric component has been demonstrated to infer appliance usage at home. • How about the magnetic component? • Magnetometer • The magnetometer measures the strength and the direction of the earth’s magnetic field in 3D space. • It is mainly used for outdoor navigation (referred to as compass). • Magnetometer can also be used for detecting magnets and ferromagnetic materials (referred to as metal detector).

  4. Key Observation • More Interestingly • When household appliances are in operation, the magnetic field around them presents a different pattern compared to the scenarios when these devices are turned off. Hair Dryer is OFF Hair Dryer is ON • These changes exhibit different patterns for different devices and act as the signatures of the devices.

  5. Our Framework Overall Magnetic Field Strength Sliding Window at 6s Feature Extraction Standard Deviation, Mean Derivatives, Mean Crossing Rate, Dominant Frequency, Dominant Frequency Magnitude, Energy, Spectral Entropy Magnetometer Classification

  6. Evaluation • Sensing Hardware and Software • Hardware: 3-axis magnetometer in iPhone 4GS, sampling at 15Hz. • Software: techBASIC mobile application. • Data analysis is performed using MATLAB. • Household Activities Laptop Hair Dryer TV Mobile Phone Microwave

  7. Preliminary Results • Scatter Plots • Recognition Accuracy Statistical Features Energy Features

  8. Limitations and Future Work • Sampling Rate is LOW • High frequency components in the magnetic field signal that may contain important information are not captured. • Test on More Appliances • Combine with Other Environmental Sensors • light sensor, temperature sensor, and motion sensors

  9. Any Questions?

  10. Thank You

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