Abstract
Knee joint function deterioration significantly impacts quality of life. This study developed estimation models for ten knee indicators using data from in-shoe motion sensors to assess knee movement during everyday activities. Sixty-six healthy young participants were involved, and multivariate linear regression was employed to construct the models. The results showed that eight out of ten models achieved a "fair" to "good" agreement based on intra-class correlation coefficients (ICCs), with three knee joint angle indicators reaching the "fair" agreement. One temporal indicator model displayed a "good" agreement, while another had a "fair" agreement. For the angular jerk cost indicators, three out of four attained a "fair" or "good" agreement. The model accuracy was generally acceptable, with the mean absolute error ranging from 0.54 to 0.75 times the standard deviation of the true values and errors less than 1% from the true mean values. The significant predictors included the sole-to-ground angles, particularly the foot posture angles in the sagittal and frontal planes. These findings support the feasibility of estimating knee function solely from foot motion data, offering potential for daily life monitoring and rehabilitation applications. However, discrepancies in the two models were influenced by the variance in the baseline knee flexion and sensor placement. Future work will test these models on older and osteoarthritis-affected individuals to evaluate their broader applicability, with prospects for user-tailored rehabilitation applications. This study is a step towards simplified, accessible knee health monitoring through wearable technology.