Ground reaction force estimation using an insole-type pressure mat and joint kinematics during walking

Kinetic analysis of walking requires joint kinematics and ground reaction force (GRF) measurement, which are typically obtained from a force plate. GRF is difficult to measure in certain cases such as slope walking, stair climbing, and track running. Nevertheless, estimating GRF continues to be of great interest for simulating human walking. The purpose of the study was to develop reaction force models placed on the sole of the foot to estimate full GRF when only joint kinematics are provided (Type-I), and to estimate ground contact shear forces when both joint kinematics and foot pressure are provided (Type-II and Type-II-val). The GRF estimation models were attached to a commercial full body skeletal model using the AnyBody Modeling System, which has an inverse dynamics-based optimization solver. The anterior–posterior shear force and medial–lateral shear force could be estimated with approximate accuracies of 6% BW and 2% BW in all three methods, respectively. Vertical force could be estimated in the Type-I model with an accuracy of 13.75% BW. The accuracy of the force estimation was the highest during the mid-single-stance period with an average RMS for errors of 3.10% BW, 1.48% BW, and 7.48% BW for anterior–posterior force, medial–lateral force, and vertical force, respectively. The proposed GRF estimation models could predict full and partial GRF with high accuracy. The design of the contact elements of the proposed model should make it applicable to various activities where installation of a force measurement system is difficult, including track running and treadmill walking.     

Model of loadings acting on the femoral bone during gait

An evaluation of the model of loadings acting on the femoral bone during the whole gait cycle was the main aim of the paper. A computer simulation of the musculoskeletal system based on the gait data collected during gait was used to determine the muscle forces as well as the hip joint reaction. Kinematic parameters as well as the ground reaction force for ninety-nine healthy persons of both sexes (18–36 years old) who had no history of musculoskeletal disease were registered during normal gait with preferred speed and used as inputs for musculoskeletal modelling and numerical simulation with the use of the AnyBody software. Time waveforms of the values of force generated by 21 muscles having attachments on the femoral bone as well as the hip joint reaction force were obtained. Directions of particular forces were presented using a femoral coordinate system. Attachment points for all muscle forces were obtained on the basis of the unscaled standard model with the length of the femur equal to 0.41 m. The presented model of loadings acting on the femoral bone element can be useful for the biomechanical analysis of bone development and remodelling as well as for the optimisation of implant or bone stabilizer design and pre-clinical testing.     

Muscle and external load contribution to knee joint contact loads during normal gait

Large knee adduction moments during gait have been implicated as a mechanical factor related to the progression and severity of tibiofemoral osteoarthritis and it has been proposed that these moments increase the load on the medial compartment of the knee joint. However, this mechanism cannot be validated without taking into account the internal forces and moments generated by the muscles and ligaments, which cannot be easily measured. Previous musculoskeletal models suggest that the medial compartment of the tibiofemoral joint bears the majority of the tibiofemoral load, with the lateral compartment unloaded at times during stance. Yet these models did not utilise explicitly measured muscle activation patterns and measurements from an instrumented prosthesis which do not portray lateral compartment unloading. This paper utilised an EMG-driven model to estimate muscle forces and knee joint contact forces during healthy gait. Results indicate that while the medial compartment does bear the majority of the load during stance, muscles provide sufficient stability to counter the tendency of the external adduction moment to unload the lateral compartment. This stability was predominantly provided by the quadriceps, hamstrings, and gastrocnemii muscles, although the contribution from the tensor fascia latae was also significant. Lateral compartment unloading was not predicted by the EMG-driven model, suggesting that muscle activity patterns provide useful input to estimate muscle and joint contact forces.     

An image-based kinematic model of the tibiotalar and subtalar joints and its application to gait analysis in children with Juvenile Idiopathic Arthritis

In vivo estimates of tibiotalar and the subtalar joint kinematics can unveil unique information about gait biomechanics, especially in the presence of musculoskeletal disorders affecting the foot and ankle complex. Previous literature investigated the ankle kinematics on ex vivo data sets, but little has been reported for natural walking, and even less for pathological and juvenile populations. This paper proposes an MRI-based morphological fitting methodology for the personalised definition of the tibiotalar and the subtalar joint axes during gait, and investigated its application to characterise the ankle kinematics in twenty patients affected by Juvenile Idiopathic Arthritis (JIA). The estimated joint axes were in line with in vivo and ex vivo literature data and joint kinematics variation subsequent to inter-operator variability was in the order of 1°. The model allowed to investigate, for the first time in patients with JIA, the functional response to joint impairment. The joint kinematics highlighted changes over time that were consistent with changes in the patient’s clinical pattern and notably varied from patient to patient. The heterogeneous and patient-specific nature of the effects of JIA was confirmed by the absence of a correlation between a semi-quantitative MRI-based impairment score and a variety of investigated joint kinematics indexes. In conclusion, this study showed the feasibility of using MRI and morphological fitting to identify the tibiotalar and subtalar joint axes in a non-invasive patient-specific manner. The proposed methodology represents an innovative and reliable approach to the analysis of the ankle joint kinematics in pathological juvenile populations.     

Hip joint muscle forces during gait in patients with femoroacetabular impingement syndrome are associated with patient reported outcomes and cartilage composition

Femoroacetabular impingement syndrome (FAIS) consists of abnormal hip joint morphology and pain during activities of daily living. Abnormal gait mechanics and potentially abnormal muscle forces within FAI patients leads to articular cartilage damage. Therefore, there is a necessity to understand the effects of FAI on hip joint muscle forces during gait and the link between muscle forces, patient reported outcomes (PRO) and articular cartilage health. The purposes of this study were to assess: (1) hip muscle forces between FAI patients and healthy controls and (2) the associations between hip muscle forces with PRO and cartilage composition (T_1ρ/T₂ mapping) within FAI patients. Musculoskeletal simulations were used to estimate peak muscle forces during the stance phase of gait in 24 FAI patients and 24 healthy controls. Compared to controls, FAI patients ambulated with lower vasti (30% body-weight, p = 0.01) and higher sartorius (4.0% body-weight, p < 0.01) forces. Within FAI patients, lower peak gluteus medius, gluteus minimus, sartorius and iliopsoas forces were associated with worse hip joint pain and function (R = 0.43–0.70, p = 0–0.04), while lower muscle forces were associated with increased T_1ρ and T₂ values (i.e. altered cartilage composition) within the hip joint cartilage (R = −0.44 to −0.58, p = 0.006–0.05). Although FAI patients demonstrate abnormal muscle forces, it is unknown whether or not these altered muscle force patterns are associated with pain avoidance or weak musculature. Further investigation is required in order to better understand the effects of FAI on hip joint muscle forces and the associations with hip joint cartilage degeneration.     

Predicting muscle forces in gait from EMG signals and musculotendon kinematics

An EMG-driven muscle model for determining muscle force-time histories during gait is presented. The model, based on Hill's equation (1938), incorporates morphological data and accounts for changes in musculotendon length, velocity, and the level of muscle excitation for both concentric and eccentric contractions. Musculotendon kinematics were calculated using three-dimensional cinematography with a model of the musculoskeletal system. Muscle force-length-EMG relations were established from slow isokinetic calibrations. Walking muscle force-time histories were determined for two subjects. Joint moments calculated from the predicted muscle forces were compared with moments calculated using a linked segment, inverse dynamics approach. Moment curve correlations ranged from r = 0.72 to R = 0.97 and the root mean square (RMS) differences were from 10 to 20 Nm. Expressed as a relative RMS, the moment differences ranged from a low of 23% at the ankle to a high of 72% at the hip. No single reason for the differences between the two moment curves could be identified. Possible explanations discussed include the linear EMG-to-force assumption and how well the EMG-to-force calibration represented excitation for the whole muscle during gait, assumptions incorporated in the muscle modeling procedure, and errors inherent in validating joint moments predicted from the model to moments calculated using linked segment, inverse dynamics. The closeness with which the joint moment curves matched in the present study supports using the modeling approach proposed to determine muscle forces in gait.     

Combining muscle synergies and biomechanical analysis to assess gait in stroke patients

The understanding of biomechanical deficits and impaired neural control of gait after stroke is crucial to prescribe effective customized treatments aimed at improving walking function. Instrumented gait analysis has been increasingly integrated into the clinical practice to enhance precision and inter-rater reliability for the assessment of pathological gait. On the other hand, the analysis of muscle synergies has gained relevance as a novel tool to describe the neural control of walking. Since muscle synergies and gait analysis capture different but equally important aspects of walking, we hypothesized that their combination can improve the current clinical tools for the assessment of walking performance.To test this hypothesis, we performed a complete bilateral, lower limb biomechanical and muscle synergies analysis on nine poststroke hemiparetic patients during overground walking. Using stepwise multiple regression, we identified a number of kinematic, kinetic, spatiotemporal and synergy-related features from the paretic and non-paretic side that, combined together, allow to predict impaired walking function better than the Fugl-Meyer Assessment score. These variables were time of peak knee flexion, VAF_total values, duration of stance phase, peak of paretic propulsion and range of hip flexion. Since these five variables describe important biomechanical and neural control features underlying walking deficits poststroke, they may be feasible to drive customized rehabilitation therapies aimed to improve walking function.This paper demonstrates the feasibility of combining biomechanical and neural-related measures to assess locomotion performance in neurologically injured individuals.     

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.     

Intervertebral reaction force prediction using an enhanced assembly of OpenSim models

OpenSim offers a valuable approach to investigating otherwise difficult to assess yet important biomechanical parameters such as joint reaction forces. Although the range of available models in the public repository is continually increasing, there currently exists no OpenSim model for the computation of intervertebral joint reactions during flexion and lifting tasks. The current work combines and improves elements of existing models to develop an enhanced model of the upper body and lumbar spine. Models of the upper body with extremities, neck and head were combined with an improved version of a lumbar spine from the model repository. Translational motion was enabled for each lumbar vertebrae with six controllable degrees of freedom. Motion segment stiffness was implemented at lumbar levels and mass properties were assigned throughout the model. Moreover, body coordinate frames of the spine were modified to allow straightforward variation of sagittal alignment and to simplify interpretation of results. Evaluation of model predictions for level L1–L2, L3–L4 and L4–L5 in various postures of forward flexion and moderate lifting (8 kg) revealed an agreement within 10% to experimental studies and model-based computational analyses. However, in an extended posture or during lifting of heavier loads (20 kg), computed joint reactions differed substantially from reported in vivo measures using instrumented implants. We conclude that agreement between the model and available experimental data was good in view of limitations of both the model and the validation datasets. The presented model is useful in that it permits computation of realistic lumbar spine joint reaction forces during flexion and moderate lifting tasks. The model and corresponding documentation are now available in the online OpenSim repository.     

Cross-correlations between gluteal muscle thickness derived from ultrasound imaging and hip biomechanics during walking gait

Ultrasound imaging (USI) of muscle thickness offers different insights into musculoskeletal function than kinematics, kinetics, and surface electromyography (sEMG), however it is unknown how USI-derived measures correlate to traditional measures during walking. The purpose of this study was to compare USI-derived gluteus maximus (GMAX) and medius (GMED) thickness measures to tri-planar hip kinematics and kinetics, and GMED thickness to sEMG amplitude. Fourteen females walked on a treadmill at 1.34 m/s. GMAX and GMED thickness, hip tri-planar kinematics, kinetics, and GMED sEMG were simultaneously recorded. USI-derived thickness measures were compared to other biomechanical outcomes using cross-correlation analyses, computed at each 1% (11-ms) of the gait cycle with lag times from −20% to 20%. GMED and GMAX thickness measures were most strongly correlated with hip extension and abduction angles at 150–220-ms lags (cross-correlation coefficients [CCF]: −0.34; −0.83). GMED thickness was most correlated to abduction and external rotation moments simultaneously (CCF: −0.28; −0.47). GMAX thickness and flexion moments were most strongly correlated at a 66-ms lag (CCF: 0.33). GMED sEMG amplitude was most strongly correlated to muscle thickness at a 99-ms lag (CCF: 0.39). These results elucidate the unique information provided from USI-derived measures of gluteal muscle thickness during walking.     

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.     

A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon,joint contact,ligament and bone forces during gait

Musculo-tendon forces and joint reaction forces are typically estimated using a two-step method, computing first the musculo-tendon forces by a static optimization procedure and then deducing the joint reaction forces from the force equilibrium. However, this method does not allow studying the interactions between musculo-tendon forces and joint reaction forces in establishing this equilibrium and the joint reaction forces are usually overestimated. This study introduces a new 3D lower limb musculoskeletal model based on a one-step static optimization procedure allowing simultaneous musculo-tendon, joint contact, ligament and bone forces estimation during gait. It is postulated that this approach, by giving access to the forces transmitted by these musculoskeletal structures at hip, tibiofemoral, patellofemoral and ankle joints, modeled using anatomically consistent kinematic models, should ease the validation of the model using joint contact forces measured with instrumented prostheses. A blinded validation based on four datasets was made under two different minimization conditions (i.e., C1 – only musculo-tendon forces are minimized, and C2 – musculo-tendon, joint contact, ligament and bone forces are minimized while focusing more specifically on tibiofemoral joint contacts). The results show that the model is able to estimate in most cases the correct timing of musculo-tendon forces during normal gait (i.e., the mean coefficient of active/inactive state concordance between estimated musculo-tendon force and measured EMG envelopes was C1: 65.87% and C2: 60.46%). The results also showed that the model is potentially able to well estimate joint contact, ligament and bone forces and more specifically medial (i.e., the mean RMSE between estimated joint contact force and in vivo measurement was C1: 1.14BW and C2: 0.39BW) and lateral (i.e., C1: 0.65BW and C2: 0.28BW) tibiofemoral contact forces during normal gait. However, the results remain highly influenced by the optimization weights that can bring to somewhat aphysiological musculo-tendon forces.     

Muscle recruitment strategies can reduce joint loading during level walking

Joint inflammation, with consequent cartilage damage and pain, typically reduces functionality and affects activities of daily life in a variety of musculoskeletal diseases. Since mechanical loading is an important determinant of the disease process, a possible conservative treatment is the unloading of joints. In principle, a neuromuscular rehabilitation program aimed to promote alternative muscle recruitments could reduce the loads on the lower-limb joints during walking. The extent of joint load reduction one could expect from this approach remains unknown. Furthermore, assuming significant reductions of the load on the affected joint can be achieved, it is unclear whether, and to what extent, the other joints will be overloaded. Using subject-specific musculoskeletal models of four different participants, we computed the muscle recruitment strategies that minimised the hip, knee and ankle contact force, and predicted the contact forces such strategies induced at the other joints. Significant reductions of the peak force and impulse at the knee and hip were obtained, while only a minimal effect was found at the ankle joint. Adversely, the peak force and the impulse in non-targeted joints increased when aiming to minimize the load in an adjacent joint. These results confirm the potential of alternative muscle recruitment strategies to reduce the loading at the knee and the hip, but not at the ankle. Therefore, neuromuscular rehabilitation can be targeted to reduce the loading at affected joints but must be considered carefully in patients with multiple joints affected due to the potential adverse effects in non-targeted joints.     

A lower extremity model for muscle-driven simulation of activity using explicit finite element modeling

A key strength of computational modeling is that it can provide estimates of muscle, ligament, and joint loads, stresses, and strains through non-invasive means. However, simulations that can predict the forces in the muscles during activity while maintaining sufficient complexity to realistically represent the muscles and joint structures can be computationally challenging. For this reason, the current state of the art is to apply separate rigid-body dynamic and finite-element (FE) analyses in series. However, the use of two or more disconnected models often fails to capture key interactions between the joint-level and whole-body scales. Single framework MSFE models have the potential to overcome the limitations associated with disconnected models in series. The objectives of the current study were to create a multi-scale FE model of the human lower extremity that combines optimization, dynamic muscle modeling, and structural FE analysis in a single framework and to apply this framework to evaluate the mechanics of healthy knee specimens during two activities. Two subject-specific FE models (Model 1, Model 2) of the lower extremity were developed in ABAQUS/Explicit including detailed representations of the muscles. Muscle forces, knee joint loading, and articular contact were calculated for two activities using an inverse dynamics approach and static optimization. Quadriceps muscle forces peaked at the onset of chair rise (2174 N, 1962 N) and in early stance phase (510 N, 525 N), while gait saw peak forces in the hamstrings (851 N, 868 N) in midstance. Joint forces were similar in magnitude to available telemetric patient data. This study demonstrates the feasibility of detailed quasi-static, muscle-driven simulations in an FE framework.     

Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis

We developed a Kalman smoothing algorithm to improve estimates of joint kinematics from measured marker trajectories during motion analysis. Kalman smoothing estimates are based on complete marker trajectories. This is an improvement over other techniques, such as the global optimisation method (GOM), Kalman filtering, and local marker estimation (LME), where the estimate at each time instant is only based on part of the marker trajectories. We applied GOM, Kalman filtering, LME, and Kalman smoothing to marker trajectories from both simulated and experimental gait motion, to estimate the joint kinematics of a ten segment biomechanical model, with 21 degrees of freedom. Three simulated marker trajectories were studied: without errors, with instrumental errors, and with soft tissue artefacts (STA). Two modelling errors were studied: increased thigh length and hip centre dislocation. We calculated estimation errors from the known joint kinematics in the simulation study. Compared with other techniques, Kalman smoothing reduced the estimation errors for the joint positions, by more than 50% for the simulated marker trajectories without errors and with instrumental errors. Compared with GOM, Kalman smoothing reduced the estimation errors for the joint moments by more than 35%. Compared with Kalman filtering and LME, Kalman smoothing reduced the estimation errors for the joint accelerations by at least 50%. Our simulation results show that the use of Kalman smoothing substantially improves the estimates of joint kinematics and kinetics compared with previously proposed techniques (GOM, Kalman filtering, and LME) for both simulated, with and without modelling errors, and experimentally measured gait motion.     

Muscle endurance time estimation during isometric training using electromyogram and supervised learning

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. Study Objective: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. Method: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. Results: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. Conclusion: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.     

Improving the upper-limb force feasible set evaluation by muscles maximal isometric force identification and cocontraction factors

In order to improve the evaluation of the force feasible set (FFS) of the upper-limb which is of great interest in the biomechanics field, this study proposes two additional techniques. The first one is based on the identification of the maximal isometric force (MIF) of Hill-based muscles models from sEMG and isometric force measurements at the hand. The second one considers muscles cocontraction.The FFS was computed with an upper-limb musculoskeletal model in three different cases. The first one (M1) considered binary muscular activation and a simple MIF scaling method based on the weight and muscle length of the subject. The second one (M2) introduces cocontraction factors determined from sEMG. The third one (M3) considers the cocontraction factors and the MIF identification. Finally, M1, M2 and M3 are compared with end-effector force measurement.M3 outperforms the two other methods on FFS prediction demonstrating the validity and the usefulness of MIF identification and the consideration of the cocontraction factors.     

Muscle synergies are similar when typically developing children walk on a treadmill at different speeds and slopes

BackgroundThe aim of this study was to determine whether changes in synergies relate to changes in gait while walking on a treadmill at multiple speeds and slopes. The hypothesis was that significant changes in movement pattern would not be accompanied by significant changes in synergies, suggesting that synergies are not dependent on the mechanical constraints but are instead neurological in origin.MethodsSixteen typically developing children walked on a treadmill for nine combinations (stages) of different speeds and slopes while simultaneously collecting kinematics, kinetics, and surface electromyography (EMG) data. The kinematics for each stride were summarized using a modified version of the Gait Deviation Index that only includes the sagittal plane. The kinetics for each stride were summarized using a modified version of the Gait Deviation Index – Kinetic which includes sagittal plane moments and powers. Within each synergy group, the correlations of the synergies were calculated between the treadmill stages.ResultsWhile kinematics and kinetics were significantly altered at the highest slope compared to level ground when walking on a treadmill, synergies were similar across stages.ConclusionsThe high correlations between synergies across stages indicate that neuromuscular control strategies do not change as children walk at different speeds and slopes on a treadmill. However, the multiple significant differences in kinematics and kinetics between stages indicate real differences in movement pattern. This supports the theory that synergies are neurological in origin and not simply a response to the biomechanical task constraints.