Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R² between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.

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.

Musculoskeletal modelling pipelines typically use generic models scaled to individual's anthropometry. The ability to represent variations in bone or joint geometry and alignment is highly limited. This may have a large effect, particularly when modelling contact between articular surfaces such as for the knee where articular contact mechanics are used to determine joint kinematics and the resulting cartilage contact pressures and locations. Here we describe a developed open-source framework for the personalisation of such models and compare dynamic simulation outputs. The framework involves three main steps: (1) positions personalised geometries from magnetic resonance imaging and replaces generic bone and contact geometries. (2) Repositions muscle and ligament attachments and via points and optimisation of wrapping surfaces to ensure physiological lengthening behaviour. Finally, (3) muscle and ligament properties are calibrated to ensure physiological behaviour. Following model creation, dynamic simulations from a single participant and gait trial were compared. Small changes in knee adduction/abduction and rotation angles were observed between models. Joint moment differences however were present in not only the knee but also hip and ankle joints. These differences resulted in changes in both the magnitude and location of knee joint contact pressure. The framework developed is automated and requires only minimal user interaction and is built using open-source software packages which can be freely downloaded and installed. The adoption of such personalised modelling approaches facilitates patient specific modelling and may provide more detailed information regarding disease progression, patient stratification and facilitate personalised rehabilitation and treatment planning.

At the time of return-to-sport, anterior cruciate ligament reconstructed athletes still show altered neuromechanics in their injured leg during single leg hopping tasks. Part of these alterations can be magnified when these athletes are fatigued. So far, little is known whether fatigue-induced landing alterations persist after return-to-sport. Therefore, the aim of this study was to evaluate whether these alterations persist in the six months following return-to-sport. Sixteen anterior cruciate ligament reconstructed athletes performed five unilateral hop tasks before and after a fatigue protocol. The hop tasks were executed at three different time points (return-to-sport, 3 and 6 months post-return-to-sport). A 2-by-3 repeated measures ANOVA was performed to evaluate whether fatigue-induced landing alterations persisted 3 and 6 months following return-to-sport. At 6 months following return-to-sport, fatigue still induces a reduction in hamstring medialis activation and an increase in the knee abduction moment during a vertical hop with 90-degree inward rotation. Most fatigue-induced landing alterations present at the time of return-to-sport normalize after resumption of sports activities. However, a larger knee abduction moment in the injured leg after resumption of sports activities can still be observed.