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Wireless movement-tracking system could collect health and behavioral data

Radio-frequency signals can be used to track peoples' movements in their own homes.

We live in a world of wireless signals flowing around us and bouncing off our bodies. MIT researchers are now leveraging those signal reflections to provide scientists and caregivers with valuable insights into people's behavior and health.


The system, called Marko, transmits a low-power radio-frequency (RF) signal into an environment. The signal will return to the system with certain changes if it has bounced off a moving human. Novel algorithms then analyze those changed reflections and associate them with specific individuals.

The system then traces each individual's movement around a digital floor plan. Matching these movement patterns with other data can provide insights about how people interact with each other and the environment.

In a paper being presented at the Conference on Human Factors in Computing Systems this week, the researchers describe the system and its real-world use in six locations: two assisted living facilities, three apartments inhabited by couples, and one townhouse with four residents. The case studies demonstrated the system's ability to distinguish individuals based solely on wireless signals — and revealed some useful behavioral patterns.

In one assisted living facility, with permission from the patient's family and caregivers, the researchers monitored a patient with dementia who would often become agitated for unknown reasons. Over a month, they measured the patient's increased pacing between areas of their unit — a known sign of agitation. By matching increased pacing with the visitor log, they determined the patient was agitated more during the days following family visits. This shows Marko can provide a new, passive way to track functional health profiles of patients at home, the researchers say.

"These are interesting bits we discovered through data," says first author Chen-Yu Hsu, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). "We live in a sea of wireless signals, and the way we move and walk around changes these reflections. We developed the system that listens to those reflections … to better understand people's behavior and health."

The research is led by Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of the MIT Center for Wireless Networks and Mobile Computing (Wireless@MIT). Joining Katabi and Hsu on the paper are CSAIL graduate students Mingmin Zhao and Guang-He Lee and alumnus Rumen Hristov SM '16.

Predicting "tracklets" and identities

When deployed in a home, Marko shoots out an RF signal. When the signal rebounds, it creates a type of heat map cut into vertical and horizontal "frames," which indicates where people are in a three-dimensional space. People appear as bright blobs on the map. Vertical frames capture the person's height and build, while horizontal frames determine their general location. As individuals walk, the system analyzes the RF frames — about 30 per second — to generate short trajectories, called tracklets.

A convolutional neural network — a machine-learning model commonly used for image processing — uses those tracklets to separate reflections by certain individuals. For each individual it senses, the system creates two "filtering masks," which are small circles around the individual. These masks basically filter out all signals outside the circle, which locks in the individual's trajectory and height as they move. Combining all this information — height, build, and movement — the network associates specific RF reflections with specific individuals.

But to tag identities to those anonymous blobs, the system must first be "trained." For a few days, individuals wear low-powered accelerometer sensors, which can be used to label the reflected radio signals with their respective identities. When deployed in training, Marko first generates users' tracklets, as it does in practice. Then, an algorithm correlates certain acceleration features with motion features. When users walk, for instance, the acceleration oscillates with steps, but becomes a flat line when they stop. The algorithm finds the best match between the acceleration data and tracklet, and labels that tracklet with the user's identity. In doing so, Marko learns which reflected signals correlate to specific identities.

The sensors never have to be charged, and, after training, the individuals don't need to wear them again. In home deployments, Marko was able to tag the identities of individuals in new homes with between 85 and 95 percent accuracy.

Striking a good (data-collection) balance

The researchers hope health care facilities will use Marko to passively monitor, say, how patients interact with family and caregivers, and whether patients receive medications on time. In an assisted living facility, for instance, the researchers noted specific times a nurse would walk to a medicine cabinet in a patient's room and then to the patient's bed. That indicated that the nurse had, at those specific times, administered the patient's medication.

The system may also replace questionnaires and diaries currently used by psychologists or behavioral scientists to capture data on their study subjects' family dynamics, daily schedules, or sleeping patterns, among other behaviors. Those traditional recording methods can be inaccurate, contain bias, and aren't well-suited for long-term studies, where people may have to recall what they did days or weeks ago. Some researchers have started equipping people with wearable sensors to monitor movement and biometrics. But elderly patients, especially, often forget to wear or charge them. "The motivation here is to design better tools for researchers," Hsu says.

Why not just install cameras? For starters, this would require someone watching and manually recording all necessary information. Marko, on the other hand, automatically tags behavioral patterns — such as motion, sleep, and interaction — to specific areas, days, and times.

Also, video is just more invasive, Hsu adds: "Most people aren't that comfortable with being filmed all the time, especially in their own home. Using radio signals to do all this work strikes a good balance between getting some level of helpful information, but not making people feel uncomfortable."

Katabi and her students also plan to combine Marko with their prior work on inferring breathing and heart rate from the surrounding radio signals. Marko will then be used to associate those biometrics with the corresponding individuals. It could also track people's walking speeds, which is a good indicator of functional health in elderly patients.

"The potential here is immense," says Cecilia Mascolo, a professor of mobile systems in the Department of Computer Science and Technology at Cambridge University. "With respect to imaging through cameras, it offers a less data-rich and more targeted model of collecting information, which is very welcome from the user privacy perspective. The data collected, however, is still very rich, and the paper evaluation shows accuracy which can enable a number of very useful applications, for example in elderly care, medical adherence monitoring, or even hospital care."

"Yet, as a community, we need to aware of the privacy risks that this type of technology bring," Mascolo adds. Certain computation techniques, she says, should be considered to ensure the data remains private.

Reprinted with permission of MIT News. Read the original article.

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Is this proof of a dramatic shift?

Strange Maps
  • Map details dramatic shift from CNN to Fox News over 10-year period
  • Does it show the triumph of "fake news" — or, rather, its defeat?
  • A closer look at the map's legend allows for more complex analyses

Dramatic and misleading

Image: Reddit / SICResearch

The situation today: CNN pushed back to the edges of the country.

Over the course of no more than a decade, America has radically switched favorites when it comes to cable news networks. As this sequence of maps showing TMAs (Television Market Areas) suggests, CNN is out, Fox News is in.

The maps are certainly dramatic, but also a bit misleading. They nevertheless provide some insight into the state of journalism and the public's attitudes toward the press in the US.

Let's zoom in:

  • It's 2008, on the eve of the Obama Era. CNN (blue) dominates the cable news landscape across America. Fox News (red) is an upstart (°1996) with a few regional bastions in the South.
  • By 2010, Fox News has broken out of its southern heartland, colonizing markets in the Midwest and the Northwest — and even northern Maine and southern Alaska.
  • Two years later, Fox News has lost those two outliers, but has filled up in the middle: it now boasts two large, contiguous blocks in the southeast and northwest, almost touching.
  • In 2014, Fox News seems past its prime. The northwestern block has shrunk, the southeastern one has fragmented.
  • Energised by Trump's 2016 presidential campaign, Fox News is back with a vengeance. Not only have Maine and Alaska gone from entirely blue to entirely red, so has most of the rest of the U.S. Fox News has plugged the Nebraska Gap: it's no longer possible to walk from coast to coast across CNN territory.
  • By 2018, the fortunes from a decade earlier have almost reversed. Fox News rules the roost. CNN clings on to the Pacific Coast, New Mexico, Minnesota and parts of the Northeast — plus a smattering of metropolitan areas in the South and Midwest.

"Frightening map"

Image source: Reddit / SICResearch

This sequence of maps, showing America turning from blue to red, elicited strong reactions on the Reddit forum where it was published last week. For some, the takeover by Fox News illustrates the demise of all that's good and fair about news journalism. Among the comments?

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  • "The idiocracy grows."
  • "(It's) like a spreading disease."
  • "One of the more frightening maps I've seen."
For others, the maps are less about the rise of Fox News, and more about CNN's self-inflicted downward spiral:
  • "LOL that's what happens when you're fake news!"
  • "CNN went down the toilet on quality."
  • "A Minecraft YouTuber could beat CNN's numbers."
  • "CNN has become more like a high-school production of a news show."

Not a few find fault with both channels, even if not always to the same degree:

  • "That anybody considers either of those networks good news sources is troubling."
  • "Both leave you understanding less rather than more."
  • "This is what happens when you spout bullsh-- for two years straight. People find an alternative — even if it's just different bullsh--."
  • "CNN is sh-- but it's nowhere close to the outright bullsh-- and baseless propaganda Fox News spews."

"Old people learning to Google"

Image: Google Trends

CNN vs. Fox News search terms (200!-2018)

But what do the maps actually show? Created by SICResearch, they do show a huge evolution, but not of both cable news networks' audience size (i.e. Nielsen ratings). The dramatic shift is one in Google search trends. In other words, it shows how often people type in "CNN" or "Fox News" when surfing the web. And that does not necessarily reflect the relative popularity of both networks. As some commenters suggest:

  • "I can't remember the last time that I've searched for a news channel on Google. Is it really that difficult for people to type 'cnn.com'?"
  • "More than anything else, these maps show smart phone proliferation (among older people) more than anything else."
  • "This is a map of how old people and rural areas have learned to use Google in the last decade."
  • "This is basically a map of people who don't understand how the internet works, and it's no surprise that it leans conservative."

A visual image as strong as this map sequence looks designed to elicit a vehement response — and its lack of context offers viewers little new information to challenge their preconceptions. Like the news itself, cartography pretends to be objective, but always has an agenda of its own, even if just by the selection of its topics.

The trick is not to despair of maps (or news) but to get a good sense of the parameters that are in play. And, as is often the case (with both maps and news), what's left out is at least as significant as what's actually shown.

One important point: while Fox News is the sole major purveyor of news and opinion with a conservative/right-wing slant, CNN has more competition in the center/left part of the spectrum, notably from MSNBC.

Another: the average age of cable news viewers — whether they watch CNN or Fox News — is in the mid-60s. As a result of a shift in generational habits, TV viewing is down across the board. Younger people are more comfortable with a "cafeteria" approach to their news menu, selecting alternative and online sources for their information.

It should also be noted, however, that Fox News, according to Harvard's Nieman Lab, dominates Facebook when it comes to engagement among news outlets.

CNN, Fox and MSNBC

Image: Google Trends

CNN vs. Fox (without the 'News'; may include searches for actual foxes). See MSNBC (in yellow) for comparison

For the record, here are the Nielsen ratings for average daily viewer total for the three main cable news networks, for 2018 (compared to 2017):

  • Fox News: 1,425,000 (-5%)
  • MSNBC: 994,000 (+12%)
  • CNN: 706,000 (-9%)

And according to this recent overview, the top 50 of the most popular websites in the U.S. includes cnn.com in 28th place, and foxnews.com in... 27th place.

The top 5, in descending order, consists of google.com, youtube.com, facebook.com, amazon.com and yahoo.com — the latter being the highest-placed website in the News and Media category.
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