Every day tech aids fast reaction to falls among elderly

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.