  Background: Pressure mapping technology has been adapted to monitor over
prolonged periods to evaluate pressure ulcer risk in individuals during
extended lying postures. However, temporal pressure distribution signals are
not currently used to identify posture or mobility. The present study was
designed to examine the potential of an automated approach for the detection of
a range of static lying postures and corresponding transitions between
postures.Methods: Healthy subjects (n = 19) adopted a range of sagittal and
lateral lying postures. Parameters reflecting both the interactions at the
support surface and body movements were continuously monitored. Subsequently,
the derivative of each signal was examined to identify transitions between
postures. Three machine learning algorithms, namely Na{\"i}ve-Bayes, k-Nearest
Neighbors and Support Vector Machine classifiers, were assessed to predict a
range of static postures, established with a training model (n = 9) and
validated with new input from test data (n = 10).Findings: Results showed that
the derivative signals provided a means to detect transitions between postures,
with actimetry providing the most distinct signal perturbations. The accuracy
in predicting the range of postures from new test data ranged between
82%--100%, 70%--98% and 69%--100% for Na{\"i}ve-Bayes, k-Nearest Neighbors and
Support Vector Machine classifiers, respectively.Interpretation: The present
study demonstrated that detection of both static postures and their
corresponding transitions was achieved by combining machine learning algorithms
with robust parameters from two monitoring systems. This approach has the
potential to provide reliable indicators of posture and mobility, to support
personalised pressure ulcer prevention strategies.
