Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
Often some of the best breakthroughs, but also the oddest in terms of “privacy invasion” come from odd sources …
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When you think of privacy invading devices you might think about your smartphone, but it’s unlikely you’d think your Wi-Fi router was spying on you – or event capable of spying on you. And when I say spying I’m not talking about the data traffic it’s shuttling backwards and forwards, I’m talking about “seeing” what you’re up to in your own home or office.
As I’ve talked about many times before thanks to Artificial Intelligence (AI) researchers have been able to turn your humble Wi-Fi router into an active radar-like device for years now, and they’re so good at using the signals in your home to “see” what you’re up to, even through walls, that they can even sense your emotional states and whether or not you’ve taken your pain medication by, for example, the way you walk in the morning.
So, as we look at the strange world that is the future of non-privacy, you should give more thought to what your humble router’s up to. But, let’s face it, sometimes spying is bad, and other times it’s good say, for example, when these and other invasive privacy busting tools help predict then prevent your upcoming heart attack so you don’t die on the kitchen floor … As you can see it’s all a matter of perspective.
Wi-Fi routers continuously broadcast radio frequencies that your phones, tablets and computers pick up and use to get you online. As the invisible frequencies travel, they bounce off or pass through everything around them — the walls, the furniture and even you. Your movements, even breathing, slightly alter the signal’s path from the router to your device.
Those interactions don’t interrupt your internet connection, but they could signal when someone is in trouble. NIST has developed a deep learning algorithm, called BreatheSmart, that can analyze those minuscule changes to help determine whether someone in the room is struggling to breathe. And it can do so with already available Wi-Fi routers and devices. This work was recently published in IEEE Access.
In 2020 NIST scientists wanted to help doctors fight the COVID-19 pandemic. Patients were isolated; ventilators were scarce. Previous research had explored using Wi-Fi signals to sense people or movement, but these setups often required custom sensing devices, and data from these studies were very limited.
“As everybody’s world was turned upside down, several of us at NIST were thinking about what we could do to help out,” says Jason Coder, who leads NIST’s research in shared spectrum metrology. “We didn’t have time to develop a new device, so how can we use what we already have?”
Working with colleagues at the Office of Science and Engineering Labs (OSEL) in the FDA’s Center for Devices and Radiological Health, Coder and research associate Susanna Mosleh advanced a new way to use existing Wi-Fi routers to measure the breathing rate of a person in the room. In Wi-Fi, the “channel state information,” or CSI, is a set of signals sent from the client (such as a cellphone or laptop) to the access point (such as the router). The CSI signal sent by the client device is always the same, and the access point receiving the CSI signals knows what it should look like. But as the CSI signals travel through the environment, they get distorted as they bounce off things or lose strength. The access point analyzes the amount of distortion to adjust and optimize the link.
These CSI streams are small, less than a kilobyte, so it doesn’t interfere with the flow of data over the channel. The team modified the firmware on the router to ask for these CSI streams more frequently, up to 10 times per second, to get a detailed picture of how the signal was changing.
They set up a manikin used to train medical professionals in an anechoic chamber with a commercial off-the-shelf Wi-Fi router and receiver. This manikin is designed to replicate several breathing conditions, from normal respiration to abnormally slow breathing (called bradypnea), abnormally rapid breathing (tachypnea), asthma, pneumonia and chronic obstructive pulmonary diseases, or COPD.
What alters the Wi-Fi signal is the way the body moves as we breathe. Think of how your chest moves differently when you are wheezing or coughing, compared with breathing normally. As the manikin “breathed,” the movement of its chest altered the path travelled by the Wi-Fi signal. The team members recorded the data provided by the CSI streams. Although they collected a wealth of data, they still needed help to make sense of what they had gathered.
“This is where we can leverage deep learning,” Coder said.
Deep learning is a subset of artificial intelligence, a type of machine learning that mimics humans’ ability to learn from their past actions and improves the machine’s ability to recognize patterns and analyze new data.
Mosleh worked on a deep learning algorithm to comb through the CSI data, understand it, and recognize patterns that indicated different breathing problems. The algorithm, which they named BreatheSmart, successfully classified a variety of respiratory patterns simulated with the manikin 99.54% of the time.
“Most of the work that’s been done before was working with very limited data,” Mosleh says. “We were able to collect data with a lot of simulated respiratory scenarios, which contributes to the diversity of the training set that was available to the algorithm.”
There has been a lot of interest in using Wi-Fi signals for sensing applications, Coder says. He and Mosleh hope that app and software developers can use the process presented in the work as a framework to create programs to remotely monitor breathing.
“All the ways we’re gathering the data is done on software on the access point (in this case, the router), which could be done by an app on a phone,” Coder says. “This work tries to lay out how somebody can develop and test their own algorithm. This is a framework to help them get relevant information.”