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UNSW Researchers Develop Fall Predictor Sensing Device

Scientists from the University of NSW (UNSW) have developed a new sensing device, which predicts who is most likely to fall and need hospital care for injuries.

The device could help elderly people who stay in their own homes to continue to remain there for a longer time. In Australia, one out of three elderly persons suffers from falls every year, leading to medical expenses of over $850 million. This could help families to decide the amount of assistance needed by an elderly relative in the home to prevent fall or if more constant care is required by the person. The new clip on device would forecast the possibility of a person falling and injuring themselves. The device could be clipped on to a belt and operates as a sensor to determine the performance of elderly persons as they perform their everyday jobs such as climbing stairs.

A group of 68 elderly people were tested with the device along with more conventional clinical assessment tests by the UNSW scientists. In 99% of cases the results from the device was in line with that of the clinical tests which means that the sensor could prove to be a low cost method of measuring the risk levels of elderly people at their own homes instead of relying on special clinics.

Dr Stephen Redmond, who is the lead researcher, has revealed that the device was placed on the waists of elderly people to evaluate and assess while they stood up, walked three meters, came back, sat down in a chair, stood up again five times and underwent an alternate step test to replicate climbing stairs. He revealed that people with greater risk of falling performed the tasks with reduced stability. He further mentioned that in future, just by wearing the sensor for limited period of time would help to estimate the risk of falling by monitoring their activities such as walking and other day-to-day activities.

There are many fall detection devices available in the market that give an alarm when someone has fallen but none of them can predict when someone would fall.

According to Dr Redmond, the research team is hoping to obtain more funds for testing and carrying out trials of the fall predictor device. He visualized that this technology could be incorporated into the fall alarms, which are available in the market.

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