  Individuals who suffer anterior cruciate ligament (ACL) injury are at higher
risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10
to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often
does not protect against knee OA development. Accordingly, a multiscale
formulation for Data Driven Prognosis (DDP) of post ACLR is developed. Unlike
traditional predictive strategies that require controlled off-line measurements
or training for determination of constitutive parameters to derive the
transitional statistics, the proposed DDP algorithm relies solely on in situ
measurements. The proposed DDP scheme is capable of predicting onset of
instabilities. Since the need for off line testing (or training) is obviated,
it can be easily implemented for ACLR, where such controlled a priori testing
is almost impossible to conduct. The DDP algorithm facilitates hierarchical
handling of the large data set, and can assess the state of recovery in post
ACLR conditions based on data collected from stair ascent and descent exercises
of subjects. The DDP algorithm identifies inefficient knee varus motion and
knee rotation as primary difficulties experienced by some of the post ACLR
population. In such cases, levels of energy dissipation rate at the knee, and
its fluctuation may be used as measures for assessing progress after ACL
reconstruction.
