Improving ultrasound-based bone registration using the iterative closest point algorithm paired with a complex optimization solver.

The Iterative Close Point (ICP) algorithm is used for bone registrations based on ultrasound measurements. However, the ICP has been shown to suffer from local minima. The Complex optimization, as a more robust routine compared to the commonly used gradient-based algorithms, could be an alternative for solving the ICP problem. In this study, we investigated the effect of the initial estimate and the number of registration points on bone registrations achieved using the ICP and a Complex optimization routine and we compared it against using Quadratic Sequential Programming (SQP). Ultrasound measurements were performed with an A-mode probe on a bovine humerus and an ovine femur embedded into ballistic gel. Simultaneously, the bones and the probe were tracked in 3D space using retroreflective markers. Kinematic, ultrasound and geometrical data obtained from scans of the specimens and the probe served as input to a bone registrations routine. Registrations were performed using two ICP solvers for different initial estimates and number of registration points. On average, 68 % of the Complex optimization registrations had less than 1 mm translation error and less than 1° rotational error for perturbations of the initial estimate from the reference measurements compared to the 35 % of the SQP ones. Similar medians of registration errors were observed between the two methods for variations of the number of the employed registration points. Although the Complex optimization provided accurate bone registrations for all cases, the objective function could not always determine the registrations with the smallest registration error. Future research should explore methodologies to overcome this challenge.

Prediction of gait kinetics using Markerless-driven musculoskeletal modeling.

Video-based motion analysis systems are emerging in the biomechanics research community, yet there is limited exploration of kinetics prediction using RGB-markerless kinematics and musculoskeletal modeling. This project aimed to provide ground reaction force (GRF) and ground reaction moment (GRM) predictions during over-ground gait by introducing RGB-markerless kinematics into a musculoskeletal modeling framework. Full-body markerless kinematic inputs and musculoskeletal modeling were used to obtain GRF and GRM predictions which were compared to measured force plate values. The markerless-driven predictions yielded average root mean-squared error (RMSE) in the stance phase of 0.035 ± 0.009 N∙BW-1, 0.070 ± 0.014 N∙BW-1, and 0.155 ± 0.041 N∙BW-1 in the mediolateral (ML), anteroposterior (AP), and vertical (V) GRFs. This was accompanied by moderate to high correlations and interclass correlation coefficients (ICC) indicating moderate to good agreement between measured and predicted values (95% Confidence Inervals: ML = [0.479, 0.717], AP = [0.714, 0.856], V = [0.803, 0.905]). For ground reaction moments (GRM), average RMSE was 0.029 ± 0.013 Nm∙BWH-1, 0.014 ± 0.005 Nm∙BWH-1, and 0.005 ± 0.002 Nm∙BWH-1 in the sagittal, frontal, and transverse planes. Pearson correlations and ICCs indicated poor agreement between systems for GRMs (95% Confidence Intervals: Sagittal = [0.314, 0.608], Frontal = [0.006, 0.373], Transverse = [0.269, 0.570]). Currently, RMSE is larger than target thresholds set from studies using Kinect, inertial, or marker-based kinematic drivers; but methodological considerations highlighted in this work may help guide follow-up iterations. At this point, further use in research or clinical practice is cautioned until methodological considerations are addressed, although results are promising at this point.

Assessment of the effect of a total contact cast on lower limb kinematics and joint loading.

BACKGROUND: Total contact casts (TCCs) are used to immobilize and unload the foot and ankle for the rehabilitation of ankle fractures and for the management of diabetic foot complications. The kinematic restrictions imposed by TCCs to the foot and ankle also change knee and hip kinematics, however, these changes have not been quantified before. High joint loading is associated with discomfort and increased risk for injuries. To assess joint loading, the effect of the muscle forces acting on each joint must also be considered. This challenge can be overcome with the help of musculoskeletal modelling. RESEARCH QUESTION: How does a TCC affect lower extremity joint loading? METHODS: Twelve healthy participants performed gait trials with and without a TCC. Kinematic and kinetic recordings served as input to subject-specific musculoskeletal models that enabled the computation of joint angles and loading. Cast-leg interaction was modelled by means of reaction forces between a rigid, zero-mass cast segment and the segments of the lower extremity. RESULTS: and Significance: Reduced ankle, knee and hip range of motion was observed for the TCC condition. Statistical parametric mapping indicated decreased hip abduction and flexion moments during initial contact with the TCC. The anterior knee force was significantly decreased during the mid and terminal stance and the second peak of the compressive knee force was significantly reduced for the TCC. As expected, the TCC resulted in significantly reduced ankle loading. SIGNIFICANCE: This study is the first to quantify the effect of a TCC on lower limb joint loading. Its results demonstrate the efficiency of a TCC in unloading the ankle joint complex without increasing the peak loads on knee and hip. Future studies should investigate whether the observed knee and hip kinematic and kinetic differences could lead to discomfort.

Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach.

BACKGROUND: Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy RESEARCH QUESTION: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms? METHODS: Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping RESULTS: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) SIGNIFICANCE: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations.

A parametric investigation on seat/occupant contact forces and their relationship with initially perceived discomfort using a configurable seat.

The present work investigates the contact forces between sitters and seat as well as their correlations with perceived discomfort. Twelve different economy class aeroplane seat configurations were simulated using a multi-adjustable experimental seat by varying seat pan and backrest angles, as well as seat pan compressed surface. Eighteen males and 18 females, selected by their body mass index and stature, tested these configurations for two sitting postures. Perceived discomfort was significantly affected by seat parameters and posture and correlated both with normal force distribution on the seat-pan surface and with normal forces at the lumbar and head supports. Lower discomfort ratings were obtained for more evenly distributed normal forces on the seat pan. Shear force at the seat pan surface was at its lowest when sitters were allowed to self-select their seat-pan angle, supporting that a shear force should be reduced but not zeroed to improve seating comfort. Practitioner Summary: The effects of seat-pan and backrest angle, anthropometric dimensions and sitting posture on contact forces and perceived discomfort were investigated using a multi-adjustable experimental seat. In addition to preferred seat profile parameters, the present work provides quantitative guidelines on contact force requirement for improving seating comfort.