Neural network based prediction of knee contact forces for different gait speeds

Abstract

Utilizing artificial intelligence (AI) for prediction of knee joint loading in lieu of computationally expensive musculoskeletal modeling is deemed necessary for real time assessment of pathological gait. In the current study, supervised learning was used to build a feed forward artificial neural network (FFANN) to learn the mapping from kinematics to joint force space. Joint angles and knee contact forces (KCFs) were estimated through inverse dynamics during treadmill walking at speeds ranging from 3 to 7 km/h. As a training set, 1848 trials of measured ground reaction forces along with calculated joint angles and KCFs were used from 40 participants. The network’s predictive power was tested under different exposure levels to the full training set. Results indicate the increasing predictive power of the FFANN with increasing level of exposure to subject-specific data. Highest level of accuracy were found for medial KCF in the distal-proximal direction for all testing scenarios, with the highest Pearson coefficient R(mean across scenarios, R =0.99) and lowest normalized root mean square error (mean across scenarios, NRMSE = 1.39%) when the model was trained using samples from all subjects. The predictive capability of the network was substantially reduced when all trials from the testing subject were excluded from the training set, although high predictive power (R =0.87, NRMSE = 8.31%) for medial proximal-distal KCF was still observed. Findings suggest that surrogate biomechanical models produced by machine learning can predict KCFs with adequate accuracy levels, paving the way for real time analysis of gait and its consequent exploitation in clinical applications.