Author:

Keywords:

Science & Technology, Physical Sciences, Technology, Chemistry, Analytical, Engineering, Electrical & Electronic, Instruments & Instrumentation, Chemistry, Engineering, knee osteoarthritis, knee contact forces, wearable sensors, IMU, musculoskeletal modelling, OpenSim, ground reaction forces, principal component analysis, joint moments, GROUND REACTION FORCES, GAIT MODIFICATION, JOINT KINEMATICS, ADDUCTION MOMENT, IN-VIVO, PREDICTION, HEALTHY, SENSORS, Humans, Osteoarthritis, Knee, Motion Capture, Biomechanical Phenomena, Knee Joint, Walking, Gait, G0E4521N#56133910, 0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4606 Distributed computing and systems software

Abstract:

Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.