Monthly Archives: April 2017

Health issues like cognitive decline and cardiac disease

We’ve long known that blood pressure, breathing, body temperature and pulse provide an important window into the complexities of human health. But a growing body of researchsuggests that another vital sign – how fast you walk – could be a better predictor of health issues like cognitive decline, falls, and even certain cardiac or pulmonary diseases.

Unfortunately, it’s hard to accurately monitor walking speed in a way that’s both continuous and unobtrusive. Professor Dina Katabi’s group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has been working on the problem, and believes that the answer is to go wireless.

In a new paper, the team presents “WiGait,” a device that can measure the walking speed of multiple people with 95 to 99 percent accuracy using wireless signals.

The size of a small painting, the device can be placed on the wall of a person’s house and its signals emit roughly one-hundredth the amount of radiation of a standard cellphone. It builds on Katabi’s previous work on WiTrack, which analyzes wireless signals reflected off people’s bodies to measure a range of behaviors from breathing and falling to specific emotions.

“By using in-home sensors, we can see trends in how walking speed changes over longer periods of time,” says lead author and PhD student Chen-Yu Hsu. “This can provide insight into whether someone should adjust their health regimen, whether that’s doing physical therapy or altering their medications.”

WiGait is also 85 to 99 percent accurate at measuring a person’s stride length, which could allow researchers to better understand conditions like Parkinson’s disease that are characterized by reduced step size.

Hsu and Katabi developed WiGait with CSAIL PhD student Zachary Kabelac and master’s student Rumen Hristov, alongside undergraduate Yuchen Liu from the Hong Kong University of Science and Technology, and Assistant Professor Christine Liu from the Boston University School of Medicine. The team will present their paper in May at ACM’s CHI Conference on Human Factors in Computing Systems in Colorado.

How it works

Today, walking speed is measured by physical therapists or clinicians using a stopwatch. Wearables like FitBit can only roughly estimate speed based on step count, and GPS-enabled smartphones are similarly inaccurate and can’t work indoors. Cameras are intrusive and can only monitor one room. VICON motion tracking is the only method that’s comparably accurate to WiGate, but it is not widely available enough to be practical for monitoring day-to-day health changes.

Neural networks trained on visual data

Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars.

But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

Two years ago, a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) described a method for peering into the black box of a neural net trained to identify visual scenes. The method provided some interesting insights, but it required data to be sent to human reviewers recruited through Amazon’s Mechanical Turk crowdsourcing service.

At this year’s Computer Vision and Pattern Recognition conference, CSAIL researchers will present a fully automated version of the same system. Where the previous paper reported the analysis of one type of neural network trained to perform one task, the new paper reports the analysis of four types of neural networks trained to perform more than 20 tasks, including recognizing scenes and objects, colorizing grey images, and solving puzzles. Some of the new networks are so large that analyzing any one of them would have been cost-prohibitive under the old method.

The researchers also conducted several sets of experiments on their networks that not only shed light on the nature of several computer-vision and computational-photography algorithms, but could also provide some evidence about the organization of the human brain.

Neural networks are so called because they loosely resemble the human nervous system, with large numbers of fairly simple but densely connected information-processing “nodes.” Like neurons, a neural net’s nodes receive information signals from their neighbors and then either “fire” — emitting their own signals — or don’t. And as with neurons, the strength of a node’s firing response can vary.

In both the new paper and the earlier one, the MIT researchers doctored neural networks trained to perform computer vision tasks so that they disclosed the strength with which individual nodes fired in response to different input images. Then they selected the 10 input images that provoked the strongest response from each node.

A significant MIT investment in advanced manufacturing innovation

These are not your grandmother’s fibers and textiles. These are tomorrow’s functional fabrics — designed and prototyped in Cambridge, Massachusetts, and manufactured across a network of U.S. partners. This is the vision of the new headquarters for the Manufacturing USA institute called Advanced Functional Fabrics of America (AFFOA) that opened Monday at 12 Emily Street, steps away from the MIT campus.

AFFOA headquarters represents a significant MIT investment in advanced manufacturing innovation. This facility includes a Fabric Discovery Center that provides end-to-end prototyping from fiber design to system integration of new textile-based products, and will be used for education and workforce development in the Cambridge and greater Boston community. AFFOA headquarters also includes startup incubation space for companies spun out from MIT and other partners who are innovating advanced fabrics and fibers for applications ranging from apparel and consumer electronics to automotive and medical devices.

MIT was a founding member of the AFFOA team that partnered with the Department of Defense in April 2016 to launch this new institute as a public-private partnership through an independent nonprofit also founded by MIT. AFFOA’s chief executive officer is Yoel Fink. Prior to his current role, Fink led the AFFOA proposal last year as professor of materials science and engineering and director of the Research Laboratory for Electronics at MIT, with his vision to create a “fabric revolution.” That revolution under Fink’s leadership was grounded in new fiber materials and textile manufacturing processes for fabrics that see, hear, sense, communicate, store and convert energy, and monitor health.

From the perspectives of research, education, and entrepreneurship, MIT engagement in AFFOA draws from many strengths. These include the multifunctional drawn fibers developed by Fink and others to include electronic capabilities within fibers that include multiple materials and function as devices. That fiber concept developed at MIT has been applied to key challenges in the defense sector through MIT’s Institute for Soldier Nanotechnology, commercialization through a startup called OmniGuide that is now OmniGuide Surgical for laser surgery devices, and extensions to several new areas including neural probes by Polina Anikeeva, MIT associate professor of materials science and engineering. Beyond these diverse uses of fiber devices, MIT faculty including Greg Rutledge, the Lammot du Pont Professor of Chemical Engineering, have also led innovation in predictive modeling and design of polymer nanofibers, fiber processing and characterization, and self-assembly of woven and nonwoven filters and textiles for diverse applications and industries.

Rutledge coordinates MIT campus engagement in the AFFOA Institute, and notes that “MIT has a range of research and teaching talent that impacts manufacturing of fiber and textile-based products, from designing the fiber to leading the factories of the future. Many of our faculty also have longstanding collaborations with partners in defense and industry on these projects, including with Lincoln Laboratory and the Army’s Natick Soldier Research Development and Engineering Center, so MIT membership in AFFOA is an opportunity to strengthen and grow those networks.”