A smart home with walls capable of monitoring each person's health without wearable fitness trackers just got a step closer. A research group based primarily at MIT has shown how a picture-size wall sensor can detect the walking speed and stride length of people based on how their bodies interfere with radio signals. The WiGait system works by transmitting low-power radio signals and analyzing how those radio signals reflect off of people's bodies within a radius of 9 to 12 meters. It can also detect the walking patterns of multiple people and works across physical barriers such as walls within a home. According to its inventors, this "invisible" sensor could combined with wearable devices used to track other fitness measures--especially for older people with chronic conditions such as Parkinson's disease and multiple sclerosis.
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.
Machine vision coupled with artificial intelligence (AI) has made great strides toward letting computers understand images. Thanks to deep learning, which processes information in a way analogous to the human brain, machine vision is doing everything from keeping self-driving cars on the right track to improving cancer diagnosis by examining biopsy slides or x-ray images. Now some researchers are going beyond what the human eye or a camera lens can see, using machine learning to watch what people are doing on the other side of a wall. The technique relies on low-power radio frequency (RF) signals, which reflect off living tissue and metal but pass easily through wooden or plaster interior walls. AI can decipher those signals, not only to detect the presence of people, but also to see how they are moving, and even to predict the activity they are engaged in, from talking on a phone to brushing their teeth.