Predictive modelling of human walking over a complete gait cycle

An inverse dynamics multi-segment model of the body was combined with optimisation techniques to simulate normal walking in the sagittal plane on level ground. Walking is formulated as an optimal motor task subject to multiple constraints with minimisation of mechanical energy expenditure over a complete gait cycle being the performance criterion. All segmental motions and ground reactions were predicted from only three simple gait descriptors (inputs): walking velocity, cycle period and double stance duration. Quantitative comparisons of the model predictions with gait measurements show that the model reproduced the significant characteristics of normal gait in the sagittal plane. The simulation results suggest that minimising energy expenditure is a primary control objective in normal walking. However, there is also some evidence for the existence of multiple concurrent performance objectives. Keywords: Gait prediction; Inverse dynamics; Optimisation; Optimal motor tas

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Walker-Assisted Gait in Rehabilitation: A Study of Biomechanics and Instrumentation

While walkers are commonly prescribed to improve patient stability and ambulatory ability, quantitative study of the biomechanical and functional requirements for effective walker use is limited. To date no one has addressed the changes in upper extremity kinetics that occur with the use of a standard walker, which was the objective of this study. A strain gauge-based walker instrumentation system was developed for the six degree-of-freedom measurement of resultant subject hand loads. The walker dynamometer was integrated with an upper extremity biomechanical model. Preliminary system data were collected for seven healthy, right-handed young adults following informed consent. Bilateral upper extremity kinematic data were acquired with a six camera Vicon motion analysis system using a Micro-VAX workstation. Internal joint moments at the wrist, elbow, and shoulder were determined in the three clinical planes using the inverse dynamics method. The walker dynamometer system allowed characterization of upper extremity loading demands. Significantly differing upper extremity loading patterns were Identified for three walker usage methods. Complete description of upper extremity kinetics and kinematics during walker-assisted gait may provide insight into walker design parameters and rehabilitative strategies

Methodological factors affecting joint moments estimation in clinical gait analysis: A systematic review

Quantitative gait analysis can provide a description of joint kinematics and dynamics, and it is recognized as a clinically useful tool for functional assessment, diagnosis and intervention planning. Clinically interpretable parameters are estimated from quantitative measures (i.e. ground reaction forces, skin marker trajectories, etc.) through biomechanical modelling. In particular, the estimation of joint moments during motion is grounded on several modelling assumptions: (1) body segmental and joint kinematics is derived from the trajectories of markers and by modelling the human body as a kinematic chain; (2) joint resultant (net) loads are, usually, derived from force plate measurements through a model of segmental dynamics. Therefore, both measurement errors and modelling assumptions can affect the results, to an extent that also depends on the characteristics of the motor task analysed (i.e. gait speed). Errors affecting the trajectories of joint centres, the orientation of joint functional axes, the joint angular velocities, the accuracy of inertial parameters and force measurements (concurring to the definition of the dynamic model), can weigh differently in the estimation of clinically interpretable joint moments. Numerous studies addressed all these methodological aspects separately, but a critical analysis of how these aspects may affect the clinical interpretation of joint dynamics is still missing. This article aims at filling this gap through a systematic review of the literature, conducted on Web of Science, Scopus and PubMed. The final objective is hence to provide clear take-home messages to guide laboratories in the estimation of joint moments for the clinical practice

Kinematic models of lower limb joints for musculo-skeletal modelling and optimization in gait analysis

Kinematic models of lower limb joints have several potential applications in musculoskeletal modelling of the locomotion apparatus, including the reproduction of the natural joint motion. These models have recently revealed their value also for in vivo motion analysis experiments, where the soft-tissue artefact is a critical known problem. This arises at the interface between the skin markers and the underlying bone, and can be reduced by defining multibody kinematic models of the lower limb and by running optimization processes aimed at obtaining estimates of position and orientation of relevant bones. With respect to standard methods based on the separate optimization of each single body segment, this technique makes it also possible to respect joint kinematic constraints. Whereas the hip joint is traditionally assumed as a 3 degrees of freedom ball and socket articulation, many previous studies have proposed a number of different kinematic models for the knee and ankle joints. Some of these are rigid, while others have compliant elements. Some models have clear anatomical correspondences and include real joint constraints; other models are more kinematically oriented, these being mainly aimed at reproducing joint kinematics. This paper provides a critical review of the kinematic models reported in literature for the major lower limb joints and used for the reduction of soft-tissue artefact. Advantages and disadvantages of these models are discussed, considering their anatomical significance, accuracy of predictions, computational costs, feasibility of personalization, and other features. Their use in the optimization process is also addressed, both in normal and pathological subjects

Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study

The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a “soft” constraint using a penalty-based method, this elastic joint description challenges the strictness of “hard” constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO

Foot and Ankle Motion Analysis Using Dynamic Radiographic Imaging

Lower extremity motion analysis has become a powerful tool used to assess the dynamics of both normal and pathologic gait in a variety of clinical and research settings. Early rigid representations of the foot have recently been replaced with multi-segmental models capable of estimating intra-foot motion. Current models using externally placed markers on the surface of the skin are easily implemented, but suffer from errors associated with soft tissue artifact, marker placement repeatability, and rigid segment assumptions. Models using intra-cortical bone pins circumvent these errors, but their invasive nature has limited their application to research only. Radiographic models reporting gait kinematics currently analyze progressive static foot positions and do not include dynamics. The goal of this study was to determine the feasibility of using fluoroscopy to measure in vivo intra-foot dynamics of the hindfoot during the stance phase of gait. The developed fluoroscopic system was synchronized to a standard motion analysis system which included a multi-axis force platform. Custom algorithms were created to translate points of interest from 2D fluoroscopic image space to global tri-axial space. From these translated points of interest, a hindfoot specific model was developed to quantify sagittal plane talocrural and subtalar dynamics. The new hindfoot model was evaluated and applied to a pilot population of thirteen healthy adults during barefoot and toe-only rocker walking conditions. The barefoot kinematic and kinetic results compared favorably with barefoot dynamics reported by other authors. As a result of the barefoot study, it was concluded that inter-subject variability in sagittal plane kinematics was higher for the talocrural joint than the subtalar joint. The toe-only rocker analysis was the first report of hindfoot kinematics within a rocker sole shoe modification. Hindfoot kinematic inter-subject variability was significantly lower in the toe-only rocker condition when compared to barefoot results. This study represents the first use of fluoroscopy to quantify in vivo intra-foot dynamics during the stance phase of gait. Talocrural and subtalar dynamics of healthy adult subjects are reported. The technology developed for this study is capable of examining soft tissue and bony abnormalities associated with the pathologic foot. Based on the overall results of this study, it is recommended that development continue for further analysis within the clinical environment, and examination of complex in vivo foot and ankle dynamics

To what extent is joint and muscle mechanics predicted by musculoskeletal models sensitive to soft tissue artefacts?

Musculoskeletal models are widely used to estimate joint kinematics, intersegmental loads, and muscle and joint contact forces during movement. These estimates can be heavily affected by the soft tissue artefact (STA) when input positional data are obtained using stereophotogrammetry, but this aspect has not yet been fully characterised for muscle and joint forces. This study aims to assess the sensitivity to the STA of three open-source musculoskeletal models, implemented in OpenSim. A baseline dataset of marker trajectories was created for each model from experimental data of one healthy volunteer. Five hundred STA realizations were then statistically generated using a markerdependent model of the pelvis and lower limb artefact and added to the baseline data. The STA's impact on the musculoskeletal model estimates was finally quantified using a Monte Carlo analysis. The modelled STA distributions were in line with the literature. Observed output variations were comparable across the three models, and sensitivity to the STA was evident for most investigated quantities. Shape, magnitude and timing of the joint angle and moment time histories were not significantly affected throughout the entire gait cycle, whereas magnitude variations were observed for muscle and joint forces. Ranges of contact force variations differed between joints, with hip variations up to 1.8 times body weight observed. Variations of more than 30% were observed for some of the muscle forces. In conclusion, musculoskeletal simulations using stereophotogrammetry may be safely run when only interested in overall output patterns. Caution should be paid when more accurate estimated values are needed