Postural sway is a reflection of brain signals that are generated to
control a person's balance. During the process of ageing, the postural
sway changes, which increases the likelihood of a fall. Thus far,
expensive specialist equipment is required, such as a force plate, in
order to detect such changes over time, which makes the process costly
and impractical. Our long-term goal is to investigate the use of
inexpensive, everyday video technology as an alternative. This paper
describes a study that establishes a 3-way correlation between the
clinical gold standard (force plate), a highly accurate multi-camera 3D
video tracking system (Vicon) and a standard RGB video camera. To this
end, a dataset of 18 subjects performing the BESS balance test on the
force plate was recorded, while simultaneously recording the 3D Vicon
data, and the RGB video camera data. Then, using Gaussian process
regression and a recurrent neural network, models were built to predict
the lateral postural sway in the force plate data from the RGB video
data. The predicted results show high correlation with the actual force
plate signals, which supports the hypothesis that lateral postural sway
can be accurately predicted from video data alone. Detecting changes to a
person's postural sway can be used to improve elderly people's life by
monitoring the likelihood of a fall and detecting its increase well
before a fall occurs, so that countermeasures (e.g. exercises) can be
put in place to prevent falls occurring.