Abstract
Purpose: Gait analysis has become an innovative approach to assess the biomechanical adaptations due to an ACL injury. However, interpreting the large amount of data collected often requires an expert. Therefore, there is a need to develop an automatic method capable to distinguish kinetic pattern of an ACL deficient patients from an asymptomatic population.
Method: 26 ACL deficient patients and 30 asymptomatic participants took part in a treadmill gait analysis. 3D ground reaction forces (vertical, medio-lateral and anterior-posterior) were collected using the ADAL 3D treadmill. Features were extracted from the 3D ground reaction forces as a function of time and then classified by the nearest neighbour rule using a wavelet decomposition method. The classification method was tested on our data base of 56 participants.
Results: The proposed classification method obtained an accuracy of 90%. The classification accuracy per class was higher for the ACL deficient group allowing classifying correctly 25 out of 26 ACL deficient patient. 25 out of the 30 asymptomatic participants were properly classified.
Conclusion: This study shows that an automatic objective computer method could be used in a clinical setting to help diagnose an anterior cruciate ligament injury during a gait analysis evaluation. Future studies should apply this method on a larger database including data from patients with other musculoskeletal pathologies to help diagnose other injuries.
Correspondence should be addressed to CEO Doug C. Thomson. Email: doug@canorth.org
