Radar has long been used as a tool for surveillance, mapping, search and rescue, air traffic control, weather monitoring and crime prevention. Soon to be added to that list is preventative health care, specifically the detection of falls among seniors, many of which are choosing to live independently at home.
Dr. Moeness Amin, who is the director of the Center for Advanced Communications in the college of engineering at Villanova University, is working on how to bring radar technology into homes.
Since radar can penetrate through walls, it is used by the military and police in hostage situations to know how many people are inside a building and to monitor their movements.
Amin wants to use this technology and his research work on through-the-wall radar imaging to be developed into a system that can detect when people fall in their homes and warn others to come and help.
When installed in different rooms at home, the radar device can emit and receive unique frequencies depending on the motion of a person’s body. A visual representation of these frequencies called a spectogram can tell if a person is falling or not.
Different patterns of motion create different signatures on the spectogram. For instance, the spectogram pattern of someone falling forward looks different than that of someone falling backwards. Moreover, the device can be accurate enough to know whether the fall is a drop attack or if a person trips with arms flailing.
Amin says that such a device can replace cameras for monitoring. “There’s a privacy issue if you put cameras in bedrooms or bathrooms. People don’t like that,” he told The Atlantic.
He also says radar is an alternative to existing wearable medical alert devices which can be cumbersome, forgotten or misplaced.
Amin and his team are working on an algorithm called the Doppler Frequency Signature that will study and adjust a person’s unique ways of moving and positioning. The algorithm could learn a person’s habits and the way they sit, stand, walk or fall. It can recognize the pattern of someone using a walker or cane versus someone walking normally.
“I want to train the radar to say, this person is, let’s say paralyzed, so the way this person walks is different than the general population," said Amin. "So that when this person falls, the radar knows how this person falls, not how [just] anybody falls.”
The system can locate the specific location where the person falls and alert caregivers. For example, it can send a text message or call family members to say grandpa fell in the bathroom in the second floor.
The algorithm would be completed by the end of this year at which time it would be tested by a group of elderly people rather than research students. Whether it would be eventually accepted as less intrusive than cameras or co-exist side by side with wearables remain to be seen, but Amin is hopeful.
“In the future, I think the radar is going to be like a companion, living with the person, learning about the habits of the person, the way he walks, the way he sits, the way he stands,” said Amin.
Such a fall prevention system may be welcomed by the group most at risk for falls, the aged-65-and-over demographic, whose numbers are expected to double in the next three decades.
In the Middle East, those 60 years old and above will comprise 9.1 percent of the population by 2030, up from 5 percent in 2010, according to figures by Pew Research.