Smart carpet can predict falls

September 04, 2012 // By Nick Flaherty
Researchers in Manchester have used plastic optical fibres to create an underlay for a carpet that can map walking patterns in real-time and so predict falls.

The team from three academic Schools and the Photon Science Institute at the University of Manchester used a novel tomographic technique similar to hospital scanners that maps 2D images by using light propagating under the surface of the smart carpet.
Tiny electronics at the edges act as sensors and relay signals to a computer. These signals can then be analysed to show the image of the footprint and identify gradual changes in walking behaviour or a sudden incident such as a fall or trip. They can also show a steady deterioration or change in walking habits, possibly predicting a dramatic episode such as a fall.
The researchers, led by Dr Patricia Scully from The University of Manchester's Photon Science Institute, believe the carpet could be vital not only for helping people in the immediate aftermath of a fall, but also in identifying subtle changes in people's walking habits which might not be spotted by a family member or carer.
As many as 30%-40% of community dwelling older people fall each year. This is the most serious and frequent accident in the home and accounts for 50% of hospital admissions in the over 65 age group. Being able to identify changes in people's walking patterns and gait in the natural environment, such as in a corridor in a nursing home, could help identity problems earlier on.
The imaging technology is versatile enough to be used to detect the presence of chemical spillages or fire as an early-warning system.
"The carpet can gather a wide range of information about a person's condition; from biomechanical to chemical sensing of body fluids, enabling holistic sensing to provide an environment that detects and responds to changes in patient condition," said Scully. "The carpet can be retrofitted at low cost, to allow living space to adapt as the occupiers' needs evolve – particularly relevant with an aging population and for those with long term disabilities –