Prediction of Temperature and Loading History Dependent Lumbar Spine Biomechanics Under Cyclic Loading Using Recurrent Neural Networks

Abstract

Extended-duration cyclic loading of the spine is known to be correlated to lower back pain (LBP). Therefore, it is important to understand how the loading history affects the entire structural behavior of the spine, including the viscoelastic effects. Six human spinal segments (L4L5) were loaded with pure moments up to 7.5 Nm cyclically for half an hour, kept unloaded for 15 min, and loaded with three cycles. This procedure was performed in flexion-extension (FE), axial rotation (AR), and lateral bending (LB) and repeated six times per direction for a total of 18 h of testing per segment. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) was trained to predict the change in the biomechanical response under cyclic loading. A strong positive correlation between the total testing time and the ratio of the third cycle to the last cycle of the loading sequence was found (BT: tau = 0.3469, p = 0.0003, RT: tau =0.1988, p = 0.0377). The moment-range of motion (RoM) curves could be very well predicted with an RNN (R^2=0.988), including the correlation between testing time and testing temperature as inputs. This study shows successfully the feasibility of using RNNs to predict changing moment-RoM curves under cyclic moment loading.