Anterior Cruciate Ligament (ACL) injury is very prevalent in the field of orthopaedics, particularly in sports. Different parameters can be used to forecast an Anterior Cruciate Ligament (ACLR) patient's health condition. The aim of this research is to use Mahalanobis Taguchi System (MTS) methodology to identify useful biomechanical variables from multivariate parameters through Kanri Distance Calculator (KDC). This study analyzed biomechanical variables based on the knee flexion and extension moment and peak vertical ground reaction force from kinetic parameters; phase swing / stance, step and step length and gait velocity from spatial temporal parameter; range of motion from kinetic parameters. This experiment engaged 15 healthy subjects and 10 ACL reconstructed patients. Then, by optimizing the KDC, the 9 attributes are reduced to 7, which are knee flexion and extension moment, speed, step length and stance / swing phase. Key attributes such as swing time and moment of knee flexion are identified as optimal variables of impact in the population. KDC extends our understanding to the correlation of characteristics and enables individual diagnosis to be performed. Then the suitable rehabilitation protocol can be objectively suggested for quicker recovery to specific subjects.
