A neural network model to predict knee adduction moment during walking based on ground reaction force and anthropometric measurements

The external knee adduction moment (KAM) is a major variable for the evaluation of knee loading during walking, specifically in patients with knee osteoarthritis. However, assessment of the KAM is limited to locations where full motion laboratories are available. The purpose of this study was to develop and test a simple method to predict the KAM using only force plate and anthropometric measurements. Three groups of 28 knees (asymptomatic, mild osteoarthritis, and severe osteoarthritis) were studied. Walking trials were collected at different speeds using a motion capture system and a force plate. The reference KAM was calculated by inverse dynamics. For the prediction, inter-subject artificial neural networks were designed using 11 inputs coming from the ground reaction force and the mechanical axis alignment. The predicted KAM curves were similar to the reference curves with median mean absolute deviation (MAD) of 0.36%BW*Ht and median correlation coefficient of 0.966 over 756 individual trials. When comparing mean group curves, the median MAD was 0.09%BW*Ht and the median correlation coefficient 0.998. The peak values and the angular impulses extracted from the predicted and reference curves were significantly correlated, and the same significant differences were obtained among the three groups when the predicted or when the reference curves were used for 95% of the comparisons. In conclusion, this study demonstrated that a simple method using a generic artificial neural network can predict the KAM curve during walking with a high level of significance and provides a practical option for a broader evaluation of the KAM.