Proceedings ArticleDOI
MagnifiSense: inferring device interaction using wrist-worn passive magneto-inductive sensors
Edward Wang,Tien-Jui Lee,Alex Mariakakis,Mayank Goel,Sidhant Gupta,Shwetak N. Patel +5 more
- pp 15-26
TLDR
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 by analyzing near-field electromagnetic radiation from common components such as the motors, rectifiers, and modulators is presented.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.read more
Citations
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Proceedings ArticleDOI
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers
TL;DR: A custom smartwatch kernel is developed that boosts the sampling rate of a smartwatch's existing accelerometer to 4 kHz, using this new source of high-fidelity data to classify hand gestures and unlock user interface techniques that previously relied on special-purpose and/or cumbersome instrumentation.
Proceedings ArticleDOI
Sensing Fine-Grained Hand Activity with Smartwatches
Gierad Laput,Chris Harrison +1 more
TL;DR: This work explores the feasibility of sensing hand activities from commodity smartwatches, which are the most practical vehicle for achieving this vision, and highlights an underutilized, yet highly complementary contextual channel that could unlock a wide range of promising applications.
Proceedings ArticleDOI
Scenariot: Spatially Mapping Smart Things Within Augmented Reality Scenes
TL;DR: Scenariot is presented, a method that enables instant discovery and localization of the surrounding smart things while also spatially registering them with a SLAM based mobile AR system and fosters in-situ interactions with connected devices.
Journal ArticleDOI
Improved Vehicle Steering Pattern Recognition by Using Selected Sensor Data
TL;DR: A new method to reduce both the energy consumption and the computation complexity, and improve the recognition accuracy of vehicle steering patterns using the following three improvements is presented: a MultiWave filter is designed to replace the fixed sliding window, which is used to identify vehicle steering events.
Proceedings ArticleDOI
Deus EM Machina: On-Touch Contextual Functionality for Smart IoT Appliances
TL;DR: This work proposes an approach where users simply tap a smartphone to an appliance to discover and rapidly utilize contextual functionality, and builds twelve example applications, including six fully functional end-to-end demonstrations.
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