Shape-model scaling is more robust than linear scaling to marker placement error.

When reconstructing bone geometry to calculate joint kinematics, shape-model scaling can be more accurate and repeatable than linear scaling given the same anatomical landmarks. This study perturbed anatomical landmarks from optical motion capture and determined the robustness of shape-model scaling to misplaced markers compared to a traditional approach of linear scaling. We hypothesised that shape-model scaling would be less susceptible to variance in marker positions compared to linear scaling. The positions of hip joint centres and femoral/tibial segment lengths across perturbations were compared to determine each scaling method's range of geometric variation. The standard deviation (SD) of the hip joint centre location from the shape model had a maximum of 1.4 mm, compared to 4.2 mm for linear scaling. Femoral and tibial segments displayed SD's of 5.4 mm and 5.2 mm when shape-model scaled, compared to 9.2 mm and 9.5 mm with linear scaling, respectively, thus supporting our hypothesis. Geometric constraints within a shape model provide robustness to marker misplacement providing potential improvements in repeatability and data exchange.

Hip centre regression progression: Same equations, better numbers.

Accurate estimation of the hip joint centre (HJC) location is critical for modelling the kinematics and kinetics of the lower limb. Regression equations are commonly used to predict the HJC from anatomical landmarks on the pelvis, such as those published by Tylkowski et al., Andriacchi et al., Bell et al., and Seidel et al. Using a population of 159 CT-segmented pelvises, we assessed the accuracy of these methods as originally reported, and refined their parameters based on our larger cohort. We found the Tylkowski, Bell, and Seidel methods had mean Euclidean errors of 22.5, 26.4, and 17.9 mm, respectively. With new parameters for each method 'back-calculated' from our pelvic population, each method's error was reduced by an average of 69 %, with mean absolute errors of 7.9, 6.6, and 5.9 mm, respectively. For all methods, error has been reduced to below 1 cm, well below published levels for pelvic landmark estimation methods. These results highlight the need to validate and re-calibrate joint centre prediction methods on large, representative datasets to account for natural morphological variations.

Shape-model scaled gait models can neglect segment markers without consequential change to inverse kinematics results.

Inverse Kinematics (IK) is an optimisation to estimate joint angles from motion capture data, where marker trajectories and weighting strategies determine the outcome. Skin-mounted markers are subject to Soft Tissue Artefact (STA), particularly thigh markers. Our first aim was to test the effect of neglecting thigh markers on IK results across different markersets. Our second aim was to investigate inter-markerset differences using varying weighting strategies. Twenty participants participated in a treadmill walking motion capture session. Inverse kinematic analysis was performed using three markersets, termed Contemporary (segment clusters), NoThigh (Contemporary without thigh markers), and Traditional (modified Helen Hayes). Seven weighting schemes were used with varying magnitudes with each markerset. Joint angles (comprising tri-planar hip angles, and sagittal knee and ankle angles) were compared across all three conditions. NoThigh and Traditional generated joint angle results that differed from the Contemporary markerset by a median of 1.2° and 1.5°, respectively. Non-sagittal hip angles differed the most. Most average joint angle differences were smaller than previously-estimated STA error. NoThigh generated less difference from Contemporary than Traditional for the hip and knee joints. Intuitively, weighting strategies that heavily favour shared markers resulted in IK results with closer inter-markerset agreement. Thigh markers can be neglected without introducing more error than STA, and different markersets (with validation) can be compared against each other when using strategic weighting. This creates new research pathways for collaboration and data re-use, as well as freeing researchers (and participants) of their attachment to thigh markers.

Shape model constrained scaling improves repeatability of gait data.

Decisions made by gait researchers in the generation of kinematic or musculoskeletal models are a potential source of variation between researchers, leading to variable model outcomes. Statistical shape models can accurately predict bone geometry and have the potential to improve the repeatability of clinical gait analysis. The purpose of this study was to determine if using a shape model to scale segment length and joint centre locations would improve repeatability of kinematic and kinetic gait data, compared to linear scaling methods. Five participants completed a motion capture experiment, including a standing static trial and walking at a self-selected speed. Anatomical landmarks from the static trial were used by five experienced researchers to generate kinematic models using two methods; (1) linear scaling in OpenSim, and (2) shape-model scaling using our 'MAP Client' scale tool. The resulting models were used to perform an inverse kinematic and inverse dynamic analysis on the walking trials, and variation between researchers was analysed by comparing outputs from the same motion capture trial using different models. Higher variability between researchers was observed in joint angles (P < 0.001), joint moments (P < 0.005), and joint powers (P < 0.005) when using linear scaling, compared to shape-model scaling. Variation was at least three times as large for linearly-scaled models compared to shape-model scaled models. We have identified that linear scaling can lead to substantial variability in gait data across researchers, even with the same experimental data. Using a shape model to scale musculoskeletal models results in repeatable kinematic and kinetic gait data.

Haptic biofeedback induces changes in ankle push-off during walking.

BACKGROUND: Ankle push-off drives forward progression during gait. Reduced peak ankle moment and peak ankle power may contribute to the increased metabolic cost of walking observed in certain clinical populations. Biofeedback is an effective gait training tool, however biofeedback targeting ankle moment has not been previously studied. RESEARCH QUESTION: Does haptic biofeedback directly targeting ankle moment enable able-bodied adults to modulate peak ankle moment during gait? METHODS: 20 able-bodied adults participated in the study. Participants completed a 90-second baseline walking trial, followed by two 2-minute trials with haptic biofeedback. Haptic biofeedback guided participants to either increase peak ankle moment (Feedback High), or decrease peak ankle moment (Feedback Low). Ten participants received haptic biofeedback alone; the other ten participants additionally received verbal suggestions of movement strategies they could adopt during the biofeedback trials. Two-way analysis of variance was used to determine the effect of walking condition and verbal instruction on key gait parameters. RESULTS: A main effect of walking condition on peak ankle moment and peak ankle power was observed (all P < 0.001). Peak ankle moment did not change from baseline during Feedback High, however peak ankle power was increased (P < 0.001). A decrease in peak ankle moment and peak ankle power was observed during Feedback Low (all P < 0.001). Verbal instruction had a significant interaction effect with walking condition in only a limited number of parameters (all P < 0.05). SIGNIFICANCE: This study demonstrates the effects of haptic biofeedback targeting peak ankle moment during gait. While this study demonstrates that able-bodied individuals have some capacity to modulate their gait pattern in response to direct biofeedback on ankle moment, further investigation is required to develop a biofeedback paradigm that can increase peak ankle moment.