Every day tech aids fast reaction to falls among elderly

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Researchers in the US have developed a new solution that provides timely assistance to elderly people who have taken a fall.

Older people are more vulnerable and likely to fall, especially if they have a long-term health condition
Older people are more vulnerable and likely to fall, especially if they have a long-term health condition - AdobeStock

According to the US Centers for Disease Control and Prevention, falls are the leading cause of injury for adults ages 65 years and older with over 14 million older adults reporting falling every year. In the UK, around one-in-three adults over 65 and half of people over 80 will have at least one fall a year.

Now, new research from Binghamton University, State University of New York, aims to cut reaction times to falls with a human action recognition (HAR) algorithm that uses local computing power to analyse sensor data and detect abnormal movements without transmitting to an offsite processing centre.

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 low-cost edge computing.

In a paper published in the IEEE Transactions on Neural Systems and Rehabilitation Engineering, they show 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.

“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,” Chen said in a statement. “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.”

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By using devices already familiar to older people, rather than a full ‘smart home’ setup, Chen believes it gives them a better sense of control over their health as they do not need to learn new technology for the system to work.

To protect people’s privacy, RESAM is said to reduce 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.

“The most dangerous place for falls is the bathroom, but nobody wants to set up a camera there,” said Chen said.

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.

“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,” he said.

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