Project Overview

EMSense: Recognizing Handled, Uninstrumented, Electro-Mechanical Objects Using Software-Defined Radio

Most everyday electrical and electromechanical objects emit small amounts of electromagnetic (EM) noise during regular operation. When a user makes physical contact with such an object, this EM signal propagates through the user, owing to the conductivity of the human body. By modifying a small, low-cost, software-defined radio, we can detect and classify these signals in real-time, enabling robust on-touch object detection. Unlike prior work, our approach requires no instrumentation of objects or the environment; our sensor is self-contained and can be worn unobtrusively on the body. We call our technique EM-Sense and built a proof-of-concept smartwatch implementation. Our studies show that discrimination between dozens of objects is feasible, independent of wearer, time and local environment.


EM-ID: Tag-less Identification of Electrical Devices via Electromagnetic Emissions

Radio Frequency Identification technology has greatly improved asset management and inventory tracking. However, for many applications RFID tags are considered too expensive compared to the alternative of a printed bar code, which has hampered widespread adoption of RFID technology. To overcome this price barrier, our work leverages the unique electromagnetic emissions generated by nearly all electronic and electromechanical devices as a means to individually identify them. This tag-less method of radio frequency identification leverages previous work showing that it is possible to classify objects by type (i.e. phone vs. TV vs. kitchen appliance, etc). A core question is whether or not the electromagnetic emissions from a given model of device, is sufficiently unique to robustly distinguish it from its peers. We present a low cost method for extracting the EM-ID from a device along with a new classification and ranking algorithm that is capable of identifying minute differences in the EM signatures. Results show that devices as divers as electronic toys, cellphones and laptops can all be individually identified with an accuracy between 72% and 100% depending on device type. While not all electronics are unique enough for individual identifying, we present a probability estimation model that accurately predicts the performance of identifying a given device out of a population of both similar and dissimilar devices. Ultimately, EM-ID provides a zero cost method of uniquely identifying, potentially billions of electronic devices using their unique electromagnetic emissions.




Best Poster / Demo Award: "EM-ID: Tag-less Identification of Electrical Devices via Electromagnetic Emissions"; Chouchang (Jack) Yang and Alanson Sample; IEEE International Conference on RFID, 2016

Winner of the 2016 Fast Company magazine's Innovation by Design Awards: "EM-Sense"; Chris Harrison, Gierad Laput, Alanson Sample, Robert Xiao, & Jack Yang

Nominated for the Best Paper Award: "EM-ID: Tag-less Identification of Electrical Devices via Electromagnetic Emissions"; Chouchang (Jack) Yang and Alanson Sample; IEEE International Conference on RFID, 2016

best paper award

Related Publication

Collaborators and Contributors

This project originated as a joint collaboration between researchers at Disney Research, Pittsburgh and the Human-Computer Interaction Institute at CMU resulting in the initial EM-Sense paper.