Effects of the soft tissue artefact on the hip joint kinematics during unrestricted activities of daily living

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

Understanding individuals' distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that "gait signatures" describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI): 99.1-100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants' preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.

Accuracy of conventional motion capture in measuring hip joint center location and hip rotations during gait, squat, and step-up activities

Accurate measurements of hip joint kinematics are essential for improving our understanding of the effects of injury, disease, and surgical intervention on long-term hip joint health. This study assessed the accuracy of conventional motion capture (MoCap) for measuring hip joint center (HJC) location and hip joint angles during gait, squat, and step-up activities while using dynamic biplane radiography (DBR) as the reference standard. Twenty-four young adults performed six trials of treadmill walking, six body-weight squats, and six step-ups within a biplane radiography system. Synchronized biplane radiographs were collected at 50 images per second and MoCap was collected simultaneously at 100 images per second. Bone motion during each activity was determined by matching digitally reconstructed radiographs, created from subject-specific CT-based bone models, to the biplane radiographs using a validated registration process. Errors in estimating HJC location and hip angles using MoCap were quantified by the root mean squared error (RMSE) across all frames of available data. The MoCap error in estimating HJC location was larger during step-up (up to 89.3 mm) than during gait (up to 16.6 mm) or squat (up to 31.4 mm) in all three anatomic directions (all p < 0.001). RMSE in hip joint flexion (7.2°) and abduction (4.3°) during gait was less than during squat (23.8° and 8.9°) and step-up (20.1° and 10.6°) (all p < 0.01). Clinical analysis and computational models that rely on skin-mounted markers to estimate hip kinematics should be interpreted with caution, especially during activities that involve deeper hip flexion.

A frame orientation optimisation method for consistent interpretation of kinematic signals

In clinical movement biomechanics, kinematic data are often depicted as waveforms (i.e. signals), characterising the motion of articulating joints. Clinically meaningful interpretations of the underlying joint kinematics, however, require an objective understanding of whether two different kinematic signals actually represent two different underlying physical movement patterns of the joint or not. Previously, the accuracy of IMU-based knee joint angles was assessed using a six-degrees-of-freedom joint simulator guided by fluoroscopy-based signals. Despite implementation of sensor-to-segment corrections, observed errors were clearly indicative of cross-talk, and thus inconsistent reference frame orientations. Here, we address these limitations by exploring how minimisation of dedicated cost functions can harmonise differences in frame orientations, ultimately facilitating consistent interpretation of articulating joint kinematic signals. In this study, we present and investigate a frame orientation optimisation method (FOOM) that aligns reference frames and corrects for cross-talk errors, hence yielding a consistent interpretation of the underlying movement patterns. By executing optimised rotational sequences, thus producing angular corrections around each axis, we enable a reproducible frame definition and hence an approach for reliable comparison of kinematic data. Using this approach, root-mean-square errors between the previously collected (1) IMU-based data using functional joint axes, and (2) simulated fluoroscopy-based data relying on geometrical axes were almost entirely eliminated from an initial range of 0.7°-5.1° to a mere 0.1°-0.8°. Our results confirm that different local segment frames can yield different kinematic patterns, despite following the same rotation convention, and that appropriate alignment of reference frame orientation can successfully enable consistent kinematic interpretation.

A novel method for in vivo measurement of dynamic ischiofemoral space based on MRI and motion capture

Purpose: To use a novel in vivo method to simulate a moving hip model. Then, measure the dynamic bone-to-bone distance, and analyze the ischiofemoral space (IFS) of patients diagnosed with ischiofemoral impingement syndrome (IFI) during dynamic activities. Methods: Nine healthy subjects and 9 patients with IFI were recruited to collect MRI images and motion capture data. The motion trail of the hip during motion capture was matched to a personalized 3D hip model reconstructed from MRI images to get a dynamic bone model. This personalized dynamic in vivo method was then used to simulate the bone motion in dynamic activities. Validation was conducted on a 3D-printed sphere by comparing the calculated data using this novel method with the actual measured moving data using motion capture. Moreover, the novel method was used to analyze the in vivo dynamic IFS between healthy subjects and IFI patients during normal and long stride walking. Results: The validation results show that the root mean square error (RMSE) of slide and rotation was 1.42 mm/1.84° and 1.58 mm/2.19°, respectively. During normal walking, the in vivo dynamic IFS was significantly larger in healthy hips (ranged between 15.09 and 50.24 mm) compared with affected hips (between 10.16 and 39.74 mm) in 40.27%-83.81% of the gait cycle (p = 0.027). During long stride walking, the in vivo dynamic IFS was also significantly larger in healthy hips (ranged between 13.02 and 51.99 mm) than affected hips (between 9.63 and 44.22 mm) in 0%-5.85% of the gait cycle (p = 0.049). Additionally, the IFS of normal walking was significantly smaller than long stride walking during 0%-14.05% and 85.07%-100% of the gait cycle (p = 0.033, 0.033) in healthy hips. However, there was no difference between the two methods of walking among the patients. Conclusions: This study established a novel in vivo method to measure the dynamic bone-to-bone distance and was well validated. This method was used to measure the IFS of patients diagnosed with IFI, and the results showed that the IFS of patients is smaller compared with healthy subjects, whether in normal or long stride walking. Meanwhile, IFI eliminated the difference between normal and long stride walking.

Measurement error associated with gait cycle selection in treadmill running at various speeds

A common approach in the biomechanical analysis of running technique is to average data from several gait cycles to compute a 'representative mean.' However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to 'global' means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5 m s^-1, 3.5 m s^-1 and 4.5 m s^-1). 'Ground truth' values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5-30) consecutive gait cycles. We also compared representative means calculated from n (range = 5-15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the 'ground truth' mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of 'error' were observed between the 2.5 m s^-1 and 3.5 m s^-1 speeds at comparable gait cycle numbers -where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e., 5-10). At the 4.5 m s^-1 speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e., 25-30) was required to achieve low errors (i.e., 1-2 degrees) at the 4.5 m s^-1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e., < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5 m s^-1 and 3.5 m s^-1 speeds, and consistent but larger (i.e., up to 2-4 degrees) at the 4.5 m s^-1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential 'error.' Analysing a small sample (i.e., 5-10 cycles) will typically result in minimal 'error' (i.e., < 2 degrees), particularly at lower speeds (i.e., 2.5 m s^-1 and 3.5 m s^-1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential 'error' when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential 'error' introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.

Moving fluoroscopy-based analysis of THA kinematics during unrestricted activities of daily living

Introduction: Knowledge of the accurate in-vivo kinematics of total hip arthroplasty (THA) during activities of daily living can potentially improve the in-vitro or computational wear and impingement prediction of hip implants. Fluoroscopy- based techniques provide more accurate kinematics compared to skin marker-based motion capture, which is affected by the soft tissue artefact. To date, stationary fluoroscopic machines allowed the measurement of only restricted movements, or only a portion of the whole motion cycle. Methods: In this study, a moving fluoroscopic robot was used to measure the hip joint motion of 15 THA subjects during whole cycles of unrestricted activities of daily living, i.e., overground gait, stair descent, chair rise and putting on socks. Results: The retrieved hip joint motions differed from the standard patterns applied for wear testing, demonstrating that current pre-clinical wear testing procedures do not reflect the experienced in-vivo daily motions of THA. Discussion: The measured patient-specific kinematics may be used as input to in vitro and computational simulations, in order to investigate how individual motion patterns affect the predicted wear or impingement.

Effects of arch support doses on the center of pressure and pressure distribution of running using statistical parametric mapping

Insoles with an arch support have been used to address biomechanical risk factors of running. However, the relationship between the dose of support and running biomechanics remains unclear. The purpose of this study was to determine the effects of changing arch support doses on the center of pressure (COP) and pressure mapping using statistical parametric mapping (SPM). Nine arch support variations (3 heights * 3 widths) and a flat insole control were tested on fifteen healthy recreational runners using a 1-m Footscan pressure plate. The medial-lateral COP (COPML) coordinates and the total COP velocity (COPVtotal) were calculated throughout the entirety of stance. One-dimensional and two-dimensional SPM were performed to assess differences between the arch support and control conditions for time series of COP variables and pressure mapping at a pixel level, respectively. Two-way ANOVAs were performed to test the main effect of the arch support height and width, and their interaction on the peak values of the COPVtotal. The results showed that the COPVtotal during the forefoot contact and forefoot push off phases was increased by arch supports, while the COP medial-lateral coordinates remained unchanged. There was a dose-response effect of the arch support height on peak values of the COPVtotal, with a higher support increasing the first and third valleys but decreasing the third peak of the COPVtotal. Meanwhile, a higher arch support height shifted the peak pressure from the medial forefoot and rearfoot to the medial arch. It is concluded that changing arch support doses, primarily the height, systematically altered the COP velocities and peak plantar pressure at a pixel level during running. When assessing subtle modifications in the arch support, the COP velocity was a more sensitive variable than COP coordinates. SPM provides a high-resolution view of pressure comparisons, and is recommended for future insole/footwear investigations to better understand the underlying mechanisms and improve insole design.

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

BACKGROUND

Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics.

METHODOLOGY

A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area.

RESULTS

Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown.

CONCLUSIONS

Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

Optical motion capture accuracy is task-dependent in assessing wrist motion

Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA). This study examined STA during wrist motion by evaluating the agreement between OMC and biplanar videoradiography (BVR). Nine subjects completed 7 different wrist motion tasks: doorknob rotation to capture supination and pronation, radial-ulnar deviation, flexion-extension, circumduction, hammering, and pitcher pouring. BVR and OMC captured the motion simultaneously. Wrist kinematics were quantified using helical motion parameters of rotation and translation, and Bland-Altman analysis quantified the mean difference (bias) and 95% limit of agreement (LOA). The rotational bias of doorknob pronation, a median bias of -4.9°, was significantly larger than the flexion-extension (0.7°, p < 0.05) and radial-ulnar deviation (1.8°, p < 0.01) tasks. The rotational LOA range was significantly smaller in the flexion-extension task (5.9°) compared to pitcher (11.6°, p < 0.05) and doorknob pronation (17.9°, p < 0.05) tasks. The translation bias did not differ between tasks. The translation LOA range was significantly larger in circumduction (9.8°) compared to the radial-ulnar deviation (6.3°, p < 0.05) and pitcher (3.4°, p < 0.05) tasks. While OMC technology has a wide-range of successful applications, we demonstrated it has relatively poor agreement with BVR in tracking wrist motion, and that the agreement depends on the nature and direction of wrist motion.