Using Sensors to Help People Who Suffer Accidental Falls
From: IEEE Computer - 01/2014 - page 20

Two US engineers have developed a sensor-based technology that could better
detect whether a senior citizen or some other person has fallen, The system
could then summon help.

This could be very important because, according to the United Nations’ World
Health Organization, falling is a leading cause of injury or death for people
at least 65 years old, an increasingly large segment of the global population.

Some current products that help people who fall require them to put on a
sensor and push a button to call for assistance. However, they must remember
to wear the sensor.

Other products use a video camera and software to detect falls and call for
help. However, the fall must occur within the camera’s field of view. And not
everyone wants to be videotaped at all times while at home.

Two University of Utah researchers - assistant professor Neal Patwari and
graduate student Brad Mager - have developed a technology designed to address
these shortcomings.

Their approach uses a wireless network of sensors that is installed on or in
walls. Patwari and Mager decided to work with RF sensors because their
signals can penetrate walls.

The researchers sought to develop a system that could determine a person’s
horizontal and vertical orientation, including whether an individual fell or
lay down deliberately.

During testing, they placed 12 sensors on walls at a low level and 12 higher
up. They use their transceivers to send data to a computer for processing.
If there is data from only lower-level sensors, that means a person is on the
floor.

The RF sensors communicate with one another and thus can detect the nature of
a person's movements. For example, they can measure how fast someone who was
once horizontal became vertical, thereby identifying whether the person fell
or lay down.

If a fall occurred, the system could contact a designated individual, agency,
or monitoring company for help. Patwari and Mager received a US National
Science Foundation grant and have six months to show their approach's
commercial potential, at which point they'd receive a second grant. 

They are still testing the accuracy of their system, which they hope to
release commercially via Patwari's Xandem Technology within three years.

Links:
Neal Patwari
http://faculty.utah.edu/u0544128-Neal_Patwari/biography/index.html

Fall Detection
http://span.ece.utah.edu/fall

Radio Waves 'See' through Walls - Method Could Help Police, Firefighters,
  Elderly 
http://www.unews.utah.edu/old/p/100509-1.html