Gender Effects on Knee Loading and Prediction of Knee Loads Using Instrumented Insoles and Machine Learning

Abstract

Women are more likely to experience knee osteoarthritis as compared to men, but the underlying mechanisms behind this disparity are unclear. Greater knee loads, knee adduction moment, knee flexion moment, and medial joint contact force, are linked to severity and progression of knee osteoarthritis. However, it is unknown if greater knee loads in healthy, young women during activities of daily living (sit-to-stand, stand-to-sit, walking and running) can partially explain the higher prevalence of knee osteoarthritis rates in women. Although previous research showed no significant differences in peak knee adduction moment and knee flexion moment between men and women, differences in peak medial joint contact force are largely unexplored. Women also tend to take shorter steps and run slower than men. It is unknown if these differences may result in greater cumulative knee loading per unit distance traveled as compared to men. Furthermore, knee loading measurement is typically confined to a gait laboratory, yet the knee is subjected to large cyclical loads throughout daily life. The combination of machine learning techniques and wearable sensors has been shown to improve accessibility of biomechanical measurements without compromising accuracy. Therefore, the goal of this dissertation is to develop a framework for measuring these risk factors using machine learning and novel instrumented insoles, and to investigate differences in peak and cumulative per unit distance traveled knee loads between young, healthy men and women. In study 1 we developed instrumented insoles and examined insole reliability and validity. In study 2, we estimated knee loads for most activities with strong correlation coefficients and low to moderate mean absolute errors. In study 3, we found peak medial joint contact force was not significantly different across activities for men and women. Similarly, in study 4, we found no significant difference between men and women in knee loads per unit distance traveled during walking and running. These findings suggest biomechanical mechanisms alone cannot explain the disproportionate rate of knee osteoarthritis in women. However, in future research, the developed knee loading prediction models can help quantify daily knee loads and aid in reducing knee osteoarthritis risk in both men and women.