AI Senses People's Pose Through Walls
Massachusetts: X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls.
Their latest project, "RF-Pose," uses artificial intelligence (AI) to teach wireless devices to sense people's postures and movement, even from the other side of a wall.
The researchers use a neural network to analyze radio signals that bounce off people's bodies, and can then create a dynamic stick figure that walks, stops, sits and moves its limbs as the person performs those actions.
The team says that the system could be used to monitor diseases like Parkinson's and multiple sclerosis (MS), providing a better understanding of disease progression and allowing doctors to adjust medications accordingly. It could also help elderly people live more independently, while providing the added security of monitoring for falls, injuries and changes in activity patterns.
"We've seen that monitoring patients' walking speed and ability to do basic activities on their own gives healthcare providers a window into their lives that they didn't have before, which could be meaningful for a whole range of diseases," says Katabi, who co-wrote a new paper about the project. "A key advantage of our approach is that patients do not have to wear sensors or remember to charge their devices."
Besides health-care, the team says that RF-Pose could also be used for new classes of video games where players move around the house, or even in search-and-rescue missions to help locate survivors.
"Just like how cellphones and Wi-Fi routers have become essential parts of today's households, I believe that wireless technologies like these will help power the homes of the future," says Katabi, who co-wrote the new paper with PhD student and lead author Mingmin Zhao, MIT professor Antonio Torralba, postdoc Mohammad Abu Alsheikh, graduate student Tianhong Li and PhD students Yonglong Tian and Hang Zhao. They will present it later this month at the Conference on Computer Vision and Pattern Recognition (CVPR) in Salt Lake City, Utah.
Besides sensing movement, the authors also showed that they could use wireless signals to accurately identify somebody 83 percent of the time out of a line-up of 100 individuals. This ability could be particularly useful for the application of search-and-rescue operations, when it may be helpful to know the identity of specific people.
For this paper, the model outputs a 2-D stick figure, but the team is also working to create 3-D representations that would be able to reflect even smaller micromovements. For example, it might be able to see if an older person's hands are shaking regularly enough that they may want to get a check-up.
"By using this combination of visual data and AI to see through walls, we can enable better scene understanding and smarter environments to live safer, more productive lives," say the researchers.
(Indebted to various source)