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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. [ABSTRACT FROM AUTHOR]

Anatomical knee joint features and osteoarthritis (OA) severity are associated, however confirming causals link to altered knee loading is challenging. This study leverages statistical shape models (SSM) to investigate the relationship between joint shape/alignment and knee loading during gait in knee OA (KOA) patients to understand their contribution to elevated medial knee loading in OA. Musculoskeletal (MSK) models were created for the mean as well as the first eight SSM principal modes of variation (−3,−2,−1, +1, +2, +3 standard deviations for each mode) and used as input to a MSK modeling framework. Using an identical KOA gait pattern (i.e., joint kinematics and ground reaction forces), we ran simulations for each MSK model and evaluated medial compartment loading magnitude and contact distribution at the instant of first and second peak of knee joint loading. An increase in external rotation, posterior tibia translation and a decrease in medial joint space and medial femoral condylar size predisposed the medial compartment knee joint to overloading during gait. This was coupled with an anterior and medial shift in contact location with increasing external rotated tibial position and increasing posterior tibial translation with respect to the femur. Next, results also highlighted a posterior shift of the medial compartment loading location with decreasing medial joint space. This study provides important population‐based insights on how knee shape and alignment predispose individuals with KOA to elevated medial compartmental knee loading. This information can be crucial in assessing the risk for medial KOA development and progression. [ABSTRACT FROM AUTHOR]

Musculoskeletal modeling (MSKm) and simulation provide an ideal framework for estimating movement-related parameters such as joint contact forces which cannot be measured in vivo. However, these parameters are not well-documented in free-living situations as inertial measurement units (IMUs) have rarely been combined with MSKm workflows. Although IMU-based systems are valuable alternatives to camera-based systems to estimate kinematics, no robust and validated method exists to estimate ground reaction forces (GRF) thereby alleviating the need for force plates. The aim of this study was to implement and validate a workflow for GRF estimation using a combined IMU and MSKm workflow. The ability to estimate such information, when combined with MSKm would provide clinicians access to movement-related relevant to the onset/progression of musculoskeletal diseases in an ecological environment. Can IMU-based kinematics combined with an MSKm-based simulation workflow be used to accurately estimate GRF during gait? Two healthy subjects (1 man, 1 female; age: 27.5±2.2 y) walked overground (self-selected speed). MotionCapture (100 Hz) and InertialCapture (60 Hz) systems were used to record 3D-marker trajectories and body segment orientations. GRF were recorded using force plates (1000 Hz). MoCap and InCap kinematics were calculated using OpenSim [1] and sensors-to-segment calibration OpenIMUs [2], respectively. Musculoskeletal model [3] containing 35 DOFs, 49 muscles, and 5 foot contact spheres was used in both approaches. OpenSim Moco was used to track the IMU-based kinematics with two main goals: (i) minimize the error between input kinematics and simulated motion, (ii) minimize the sum of the absolute value of the muscle activations. MocoSolver was applied to compute the optimized motion and muscle activations. GRF were evaluated based on the generated forces of the foot contact spheres. Centre of pressure (COP) was estimated using a Zero Moment Point (ZMP) method [4]. RMSE of the joint angle between MoCap and InCap was less 5° for all joint angles. RMSE between InCap kinematics and Moco simulated kinematics were 1-6° for all joint angles with knee and ankle flexion/extension max error of 10°. RMSE of anterior/posterior, vertical, and medio/lateral GRF was 0.10±0.02 BW, 0.25±0.01 BW and 0.06±0.03 BW respectively, compared to measured force plate data (Fig. 1). RMSE of anterior/posterior, and medio/lateral COP was 6.7±0.9 cm and 3.8±0.4 cm respectively, compared to measured force plate data. [Display omitted] The developed workflow for the estimation of GRF based on IMU-based kinematics and OpenSim Moco provided promising results. However, the contact sphere definition strongly affects the estimated GRF with a direct effect on the calculated COP and ultimately joint moments (not shown). [ABSTRACT FROM AUTHOR]