Falling in old age contributes to considerable misery for many people. Currently, there is a lack of practical, low cost and objective methods for identifying those at risk of falls. This thesis aims to address this need.
The majority of the literature related to falls risk and balance impairment
uses force plates to quantify postural sway. The use of such devices in a
clinical setting is rare, mainly due to cost. However, some force-plate-based
commercial products have been created, e.g. the Balance Master. To align the
research in this thesis to both the literature and existing methods of assessing
postural sway, a method is proposed which can generate sway metrics from
the output of a low-cost markerless motion capture device (Kinect V2). Good
agreement was found between the proposed method and the output of the
Balance Master.
A key reason for the lack of research into falls-risk using markerless motion
capture, is the lack of an appropriate dataset. To address this issue, a dataset
of clinical movements, recorded using markerless motion capture, was created.
Named KINECAL, It contains the recordings of 90 participants, labelled by
age and falls-risk. The data provided includes depth images, 3D joint positions,
sway metrics and socioeconomic and health meta data.
Many studies have noted that postural sway increases with age and conflate
age-related changes with falls risk. However, if one examines sub-populations
of older people, such as master athletes, It is clear that this is not necessarily
true. The structure of KINECAL allows for the examination of age-related
factors and falls-risk factors simultaneously. In addition, it includes labels of
falls history, clinical impairment and comprehensive metadata.
KINECAL was used to identify sway metrics most closely associated with
falls risk, as distinct from the ageing process. Using the identified metrics,
a model was developed that can identify those who would be classified as
impaired by a range of clinical tests.
Finally, a model is proposed, which can predict fallers by placing individuals on a scale of physical impairment. An autoencoder was used to model,
healthy adult sit-to-stand movements. Using an anomaly detection approach,
an individuals level of impairment can be plotted relative to this healthy standard. Using this model, the existence of two older populations (one with a
high falls risk and one with a low falls risk) is demonstrated