With the advent of marker-based motion capture, attempts have been made to recognise
and quantify attributes of “type”, “content” and “behaviour” from the motion data.
Current work exists to obtain quick and easy identification of human motion for use in
multiple settings, such as healthcare and gaming by using activity monitors, wearable
technology and low-cost accelerometers. Yet, analysing human motion and generating
representative features to enable recognition and analysis in an efficient and comprehensive
manner has proved elusive thus far. This thesis proposes practical solutions that
are based on insights from clinicians, and learning attributes from motion capture data
itself. This culminates in an application framework that learns the type, content and
behaviour of human motion for recognition, quantitative clinical analysis and outcome
measures.
While marker-based motion capture has many uses, it also has major limitations that
are explored in this thesis, not least in terms of hardware costs and practical utilisation.
These drawbacks have led to the creation of depth sensors capable of providing robust,
accurate and low-cost solution to detecting and tracking anatomical landmarks on the
human body, without physical markers. This advancement has led researchers to develop
low-cost solutions to important healthcare tasks, such as human motion analysis as a
clinical aid in prevention care. In this thesis a variety of obstacles in handling markerless
motion capture are identified and overcome by employing parameterisation of Axis-
Angles, applying Euler Angles transformations to Exponential Maps, and appropriate
distance measures between postures.
While developing an efficient, usable and deployable application framework for clinicians,
this thesis introduces techniques to recognise, analyse and quantify human motion in the
context of identifying age-related change and mobility. The central theme of this thesis
is the creation of discriminative representations of the human body using novel encoding
and extraction approaches usable for both marker-based and marker-less motion capture
data. The encoding of the human pose is modelled based on the spatial-temporal
characteristics to generate a compact, efficient parameterisation. This combination allows
for the detection of multiple known and unknown motions in real-time. However,
in the context of benchmarking a major drawback exists, the lack of a clinically valid
and relevant dataset to enable benchmarking. Without a dataset of this type, it is difficult
to validated algorithms aimed at healthcare application. To this end, this thesis
introduces a dataset that will enable the computer science community to benchmark
healthcare-related algorithms
