Paper Info
Title
MagnifiSense: inferring device interaction using wrist-worn passive magneto-inductive sensors.
Abstract
The different electronic devices we use on a daily basis produce distinct electromagnetic radiation due to differences in their underlying electrical components. We present MagnifiSense, a low-power wearable system that uses three passive magneto-inductive sensors and a minimal ADC setup to identify the device a person is operating. MagnifiSense achieves this by analyzing near-field electromagnetic radiation from common components such as the motors, rectifiers, and modulators. We conducted a staged, in-the-wild evaluation where an instrumented participant used a set of devices in a variety of settings in the home such as cooking and outdoors such as commuting in a vehicle. MagnifiSense achieves a classification accuracy of 82.6% using a model-agnostic classifier and 94.0% using a model-specific classifier. In a 24-hour naturalistic deployment, MagnifiSense correctly identified 25 of the total 29 events, while achieving a low false positive rate of 0.65% during 20.5 hours of non-activity.
Year
DOI
Venue
2015
10.1145/2750858.2804271
ACM International Conference on Ubiquitous Computing
Keywords
DocType
Citations 
Sensor, Magnetic, Activity Recognition, Wearable Device
Conference
10
PageRank 
References 
Authors
0.52
16
6
Name
Order
Citations
PageRank
Edward Wang1506.04
Tien-Jui Lee21075.86
Alexander Mariakakis3354.00
Mayank Goel443231.80
Sidhant Gupta597252.23
Shwetak N. Patel62967211.74