A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile...
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Transcript of A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile...
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A Preliminary Study of Sensing Appliance Usage for Human Activity Recognition Using Mobile Magnetometer
Mi Zhang and Alexander A. SawchukDepartment of Electrical Engineering
University of Southern [email protected]
UbiMI workshop at ACM Ubicomp Conference, September 8, 2012
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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
Courtesy to Opportunity Project
Computer Vision RFID
Courtesy to Intel Research
Smart Meter
Courtesy to IBM
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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).
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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.
Our Framework
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Magnetometer
Sliding Window
at 6sClassificationFeature
Extraction
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Overall Magnetic Field Strength
Standard Deviation, Mean Derivatives, Mean Crossing Rate, Dominant Frequency, Dominant Frequency Magnitude, Energy, Spectral Entropy
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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
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Preliminary Results• Scatter Plots
• Recognition Accuracy
Energy Features Statistical Features
Laptop Microwave TV Hair Dryer Mobile Phone
84.3% 100% 81.6% 92.7% 83.4%
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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
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Any Questions?
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Thank You