The Relationship Between Plantarflexor Moment Arm, Muscle Activation Patterns And Gait Velocity In The Elderly

Previous research suggests a link between the onset of functional dependence, mortality, and reductions in gait velocity among elderly, associated with effects of aging on the musculoskeletal and nervous systems. Yet underlying processes contributing to these reductions are not well known. The purpose of this study was to investigate ankle structure and lower limb muscle activation to identify differences which could be related to reduced gait velocity seen in aging. No association was found between gait velocity and PFMA (r = -0.13, p = 0.627) but a low positive correlation was found between effective mechanical advantage (EMA) and medial gastrocnemius (MG-LG) bias in stance (r = 0.42, p = 0.108). The present study does not confirm links between moment arm and gait velocity or stance phase muscle bias in elderly observed previously. Elderly subjects might not modify neuromuscular control similar to younger individuals with lower EMA. Indicating the possibility of dedifferentiation and loss of complexity within a younger elderly group that was physically active. Previously suggested relationships between these variables may have task-intensity dependencies relative to the groups studied. To this point, the influence of ankle joint leverage and lower leg neuromuscular activation patterns in elderly gait decline remain unclear

Validation of an Artificial Intelligence-based Markerless Motion Capture System for Clinical Gait Analysis

Accurate quantification of human motion is essential to understand mechanisms of performance, disease, or musculoskeletal injury. Historically, the field of human movement science has been limited by the availability of these tools, which has driven innovations in this field. The state of the art has vastly grown in the last half-century with many available systems for motion analysis. Consequently, all of these systems are limited to some extent, as most require tightly controlled laboratory environments, augmented scenes, considerable expertise or expense, and difficult numerical computations. The accuracy of a multi-view markerless motion analysis system was evaluated in this dissertation to determine its efficacy for use in clinical gait analysis. Compared to a gold-standard marker-based system, excellent validity was found in spatiotemporal parameters; but only limited correlations were seen between systems in kinematic variables. These results support the use of markerless-derived spatiotemporal parameters in healthy adults 65, and in those with Parkinson&rsquo;s disease. Additionally, this work introduced a new approach for completely markerless inverse dynamics analysis, the results of which are promising for future work. Taken together, the future use of markerless technology for clinical gait analysis is feasible; but considerable work remains before this tool can be implemented in clinical practice.</p

Concurrent validity of artificial intelligence-based markerless motion capture for over-ground gait analysis: A study of spatiotemporal parameters

Gait analysis is used in research and clinical environments; yet several limitations exist in current methodologies. Markerless systems, utilizing high-speed video and artificial intelligence, eliminate most limitations encountered in marker-, depth-, or inertial sensor-based systems; however, further development is needed to improve their utility and accessibility in practice. Spatiotemporal parameters from 22 young adults were estimated during over-ground gait. Nine parameters were calculated using events determined from force plate information com-bined with foot segment tracking and from motion of the foot relative to the sacrum using marker-based and markerless tracking. Two-way mixed effects, single measurement, absolute agreement and relative consistency interclass correlation coefficients, Bland-Altman bias and limits of agreement, and Lin's concordance correlations were used to examine the validity of parameters from markerless tracking compared to parameters calculated from gait event methods using force plates and marker-based tracking. Gait speed, stride length, step length, cycle time, and step time from the markerless system all showed strong agreement with the force plate method. Other markerless-determined parameters were not as accurate. Differences in stride width are attributable to inconsistencies in foot segment definitions between models; while differences in stance time, swing time, and double limb support time were influenced by gait event methods. Mean differences in gait parameters were smaller than meaningful clinical differences in Parkinson's disease patients and within ranges of reference values for elderly subjects. Further studies are needed to determine the validity across other patient groups, but results support the continued development of markerless systems for over-ground gait analysis

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

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 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? 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 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) 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. ▪Explored use of depth sensor for in-clinic assessment of kinetics during gait.▪Estimated kinetics using Azure Kinect-driven musculoskeletal modeling approach.▪Proposed method improved VGRF and joint moment estimation compared to Kinect V2.▪SPM and model optimization are methods for further improvement of this approach

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

Monitoring joint mechanics in anterior cruciate ligament reconstruction using depth sensor-driven musculoskeletal modeling and statistical parametric mapping

•Kinetics were estimated using a single depth sensor and musculoskeletal modeling.•Kinects were compared between both ACLR limbs and dominant limbs of controls.•No differences in kinetic waveforms were found between groups in over-ground gait.•ALCR exhibited differences in kinetic waveforms during terminal stair ascent.•Evidence of compensatory strategies may be task dependent in this ACLR cohort.The incidence of anterior cruciate ligament injury and reconstruction (ACLR) may set the stage for the development of early onset osteoarthritis in these patients. Development of accessible quantitative motion capture methodologies for recurrent monitoring of knee joint loading during daily activities following ACLR is necessary. This study aimed to compare lower extremity kinetics between ACLR affected limbs, ACLR unaffected limbs, and dominant limbs of healthy control subjects during over-ground gait and stair ascent using a single depth sensor-driven musculoskeletal modeling approach. No meaningful differences were found between groups during over-ground gait in any kinetic variables. When subjected to a stair ascent task, both ACLR limbs showed greater hip extension and internal rotation moments compared to control subjects at approximately 72–79% stance. This was coincident with greater knee flexion moments in both ALCR limbs compared to control. The absence of differences during over-ground gait but presence of compensatory strategies during stair ascent, suggests task dependent recovery in this cohort who were tested at least 1-year following surgery. Importantly, this was determined using a portable low-cost motion capture method which may be attractive to professionals in sports medicine for recurrent monitoring following ACLR

Estimation of ground reaction forces during stair climbing in patients with ACL reconstruction using a depth sensor-driven musculoskeletal model

•Accuracy of stair GRFs using depth sensor-driven musculoskeletal model was assessed.•Study subjects were ACL patients following ACL reconstruction surgery.•The estimation of GRFs was highly dependent on the evaluated force component.•This method has the potential as a cost-effective tool in the clinical setting. Although stair ambulation should be included in the rehabilitation of the long-term effects of ACL injury on knee function, the assessment of kinetic parameter in the situation where stair gait can only be established using costly and cumbersome force platforms via conventional inverse dynamic analysis. Therefore, there is a need to develop a practical laboratory setup as an assessment tool of the stair gait abnormalities in lower extremity that arise from an ACL deficiency. Can the use of a single depth sensor-driven full-body musculoskeletal gait model be considered an accurate assessment tool of the ground reaction forces (GRFs) during stair climbing for patients following ACL reconstruction (ACLR) surgery? A total of 15 patients who underwent ACLR participated in this study. GRFs data during stair climbing was collected using a custom-built 3-step staircase with two embedded force platforms. A single depth sensor, commercially available and cost effective, was used to obtain participants’ depth map information to extract the full-body skeleton information. The AnyBody TM GaitFullBody model was utilized to estimate GRFs attained by 25 artificial muscle-like actuators placed under each foot. Mean differences between the measured and estimated GRFs were compared using paired samples t-tests. The ensemble curves of the GRFs were compared between both approaches during stance phase of the gait cycle. The findings of this study showed that the estimation of the GRFs produced during staircase gait using a depth sensor-driven musculoskeletal model can produce acceptable results when compared to the traditional inverse dynamics modelling approach as an alternative tool in clinical settings for individuals who had undergone ACLR. The introduced approach of full-body musculoskeletal modelling driven by a single depth sensor has the potential to be a cost-effective stair gait analysis tool for patients with ACL injury