The aim of this thesis was to make a design proposal of a pattern
recognition system. The system should be capable to detect falls, but also
steps of elderly persons with possible walking limitations. However, in
order to evaluate the step detection of elderly, it was considered necessary
to let the system detect steps of non-elderly persons with no walking
disorders as well, allowing for recognition performance comparisons between
the two step pattern types.
Two wireless sensors were developed, allowing for collection of synchronized
signal data from test persons performing fall and step movements. The
author, equipped with a sensor at the hip, was the participant in the fall
data collection. The step data collection was carried out in two tests where
both sensors, positioned at the hip and ankle, were used. The participants
in the first test were non-elderly with no walking disorders, and the
participants in the second test were elderly persons with varying walking
capabilities.
The multivariate normal density was chosen as a model for the recognition
algorithm. The combinations of features considered appropriate for input to
the algorithm were evaluated by the comparison of probability density plots.
Running test files from the data collection with known structures through
the feature extractor and algorithm generated the plots. The model
parameters used in the evaluation were calculated from training samples,
extracted with the feature-pair under test from the collected files. One
optimal feature-pair was chosen for each of the three pattern types: fall,
step (non-elderly) and step (elderly).
The system was compiled allowing for different system configurations. The
configuration parameters required for each of the three pattern types were:
the optimal feature-pair (for the type of pattern to be recognized) and the
model parameters (calculated in the feature evaluation for this
feature-pair). Threshold configurations for each pattern type were used to
get a Boolean output from the algorithm.
The resulting system was evaluated by using test files with known structure
(as documented by video recordings) from the data collection. According to
the evaluation performed, the system detects both fall and step patterns of
elderly satisfactory. In the evaluation, the detection of steps was
significantly lower for some of the test persons, compared to the group as a
whole. One person also generated more detection of non-existing steps. It is
possible that the walking patterns of these persons differ from the rest of
the group, which could explain the results. Excluding the outliers from the
step evaluation when ...
