With the Internet of Things slated to havetens of billions of connected devices by 2020, one of the most crucial design considerations for internet-connected products is figuring out how to seamlessly integrate these devices into everyday life. In this respect, teaching machines how to identify the individuals they are interacting with is paramount—it will allow for thetotal personalization of everything that is promised by the IoT. Rather than just having internet-connectedlight bulbs andrefrigerators that are sitting around waiting to get hacked, these devices will be able to recognize you and interface with you according to your preferences (something that devices like the Xbox One are already doing via facial recognition).
So far there have been a number of proposed methods for integrating human identification into smart objects, ranging from the creepy and invasive (thinkRFID chip implants orfacial recognition) to the limited and cumbersome (likefingerprint scanners). In the quest for a non-invasive yet ubiquitous mode of human identification, a team of researchers from Northwestern Polytechnic University figured out a way to use WiFi signals to ID individuals moving around in a room—with an ID accuracy upwards of 90 percent.
As the team detailed in apaper posted to arXiv earlier this month, their novel approach to human identification—which they're calling FreeSense—uses interruptions in WiFi waves to identify individuals based on body shape and motion patterns. This is accomplished by monitoring changes in the WiFi's channel state information (CSI), which is a fancy way of saying the fine-grained data about how a WiFi wave is propagating in a given space.
"Due to the difference of body shapes and motion patterns, each person can have specific influence patterns on surrounding WIFI signals while she moves indoors, generating a unique pattern on the CSI time series of the WIFI device," the team writes in its report.