Researchers in the US have developed an algorithm that could make it easier to detect falls at home using everyday devices like smartphones and laptops.
New research from Binghamton University, State University of New York aims to cut reaction times with a human action recognition (HAR) algorithm that uses local computing power to analyse sensor data and detect abnormal movements without transmitting to a processing centre offsite.
Professor Yu Chen and PhD student Han Sun from the Thomas J. Watson College of Engineering and Applied Science’s Department of Electrical and Computer Engineering designed the Rapid Response Elderly Safety Monitoring (RESAM) system to leverage the latest advancements in edge computing.
The new research shows that the RESAM system can run using a smartphone, smartwatch, laptop or desktop computer with 99 per cent accuracy and a 1.22-second response time, ranking among the most accurate methods available today.
Chen said : “When many people talk about high tech, they are discussing something cutting edge, like a fancier algorithm, a more powerful assistant to do jobs faster or having more entertainment available.
“We observed a group of people — senior citizens — who need more help but normally do not have sufficient resources or the opportunity to tell high-tech developers what they need.”
By using devices already familiar to older people, rather than a full “smart home” setup, he thinks it gives them a better sense of control over their health.
They don’t need to learn new technology for the system to be effective.
Also, to protect people’s privacy, RESAM reduces the monitored images to skeletons, which still allows analysis of key points such as arms, legs and torso to determine if someone has fallen or suffered a different accident that could lead to injury.
Chen said: “The most dangerous place for falls is the bathroom, but nobody wants to set up a camera there.
“People would hate it.”
He sees the RESAM system as a cornerstone for a wider concept he’s calling “Happy Home,” which could include thermal or infrared cameras and other sensors to remotely assess other aspects of a person’s environment and well-being.
The researcher said: “Adding more sensors can make our system more powerful, because we are not only monitoring someone’s body movements — we can monitor someone’s health with one more dimension, so we better predict if something’s going to happen before it happens.”
Another idea, which Chen is exploring is for the system to include a robot dog or similar “pet” that would keep a closer watch as someone did their daily tasks.
Last autumn, Associate Professor Shiqi Zhang from the Department of Computer Science demonstrated how a robot dog might guide someone with visual impairment through tugs on a lead.
Chen said: “You could have a conversation with the robot.
“For example, when you are heading to the bathroom, the dog may ask you, ‘Would you mind if I follow you?’
“The dog can make a better decision to move closer to monitor your status instead of having only fixed sensors in the room.”
