New sensors predict collapses in the elderly three weeks ahead of accidents

Falls are a common problem among the elderly and can lead to more serious health conditions in the long-run. New sensors that monitor older people have been trialled and were found to predict when an elderly person might fall weeks in advance.

The system, which has been pioneered by the University of Missouri, uses infra-red monitors to detect subtle changes in the way a person is moving about in their home. It has been proven to have a high degree of accuracy and can make families or health professionals aware of health changes that could eventually lead to a fall.

By measuring both the walking speed and length of a stride on a regular basis, the device can show if an elderly person’s health is deteriorating. Armed with this information, measures can be taken to establish the underlying problem that is causing the changes and stop a fall from happening in advance.

It works using a series of wall-mounted movement sensors placed in the rooms used by the elderly person. They are similar to the detectors that give video games like Kinect, on Microsoft's Xbox their functionality. When a potential problem is detected, a text or email is automatically generated to alert healthcare professionals.

Researchers have discovered that an individual’s risk of falling is increased by as much as four times if their walking speed slows down. They said that pensioners have an 86 per cent chance of collapsing within three weeks if their stride is reduced by 5.1 centimetres per second.

Official figures show that one in three pensioners have fallen once or more in the past year. Falls are among the leading causes of hip fractures in the UK and can be the result of medical conditions that have not been previously diagnosed. They cost the NHS £2.3 billion annually.

Professor Marjorie Skubic, from the University of Missouri, was the lead researcher on the project, which she presented at the American Association for the Advancement of Science's annual meeting. Her work on it began as a direct result of her mother-in-law having a fall and injuring her shoulder.

As most interventions associated with falls in the elderly happen after the event, Professor Skubic wanted to look at preventative measures. She has tested the system on 23 individuals with an average age of 85. If more trials are carried out and the sensors marketed, it could help even more elderly people in the future.