  In recent years the NHS has been having increased difficulty seeing all
low-risk patients, this includes but not limited to suspected osteoarthritis
(OA) patients. To help address the increased waiting lists and shortages of
staff, we propose a novel method of automated biomarker identification for
diagnosis of knee disorders and the monitoring of treatment progression. The
proposed method allows for the measurement and analysis of biomechanics and
analyse their clinical significance, in both a cheap and sensitive alternative
to the currently available commercial alternatives. These methods and results
validate the capabilities of standard RGB cameras in clinical environments to
capture motion and show that when compared to alternatives such as depth
cameras there is a comparable accuracy in the clinical environment. Biomarker
identification using Principal Component Analysis (PCA) allows the reduction of
the dimensionality to produce the most representative features from motion
data, these new biomarkers can then be used to assess the success of treatment
and track the progress of rehabilitation. This was validated by applying these
techniques on a case study utilising the exploratory use of local anaesthetic
applied on knee pain, this allows these new representative biomarkers to be
validated as statistically significant (p-value < 0.05).
