Evaluation of an Unloading Concept for Knee Osteoarthritis: A Pilot Study in a Small Patient Group.

Joint compressive forces have been identified as a risk factor for osteoarthritis disease progression. Therefore, unloader braces are a common treatment with the aim of relieving pain, but their effects are not clearly documented in the literature. A knee brace concept was tested with the aim of reducing joint loads and pain in knee osteoarthritis patients by applying an extension moment exclusively during the stance phase. The ideal effects were evaluated during gait based on musculoskeletal modeling of six patients, and experimental tests with a prototype brace were conducted on one patient. The effects were evaluated using electromyography measurements and musculoskeletal models to evaluate the muscle activation and knee compressive forces, respectively. The ideal brace simulations revealed a varying reduction of the first peak knee force between 3.5% and 33.8% across six patients whereas the second peak was unaffected. The prototype reduced the peak vasti muscle activation with 7.9% and musculoskeletal models showed a reduction of the first peak knee compressive force of up to 26.3%. However, the prototype brace increased the knee joint force impulse of up to 17.1% and no immediate pain reduction was observed. The reduction of the first peak knee compressive force, using a prototype on a single patient, indicates a promising effect from an applied knee extension moment for reducing knee joint loads during normal gait. However, further clinical experiments with this brace method are required to evaluate the long-term effects on both pain and disease progression in knee osteoarthritis patients.

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model.

Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for noninvasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established/widely used models. This study rigorously compares the upper extremity (UE) joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (involving multibody kinematics optimization (MKO)) with those estimated by the Vicon Plug-in Gait model (involving single-body kinematics optimization (SKO)). Ten subjects performed three trials of (different types of) reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all root-mean-squared differences less than 10 deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation coefficients were found between the two model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree with each other, albeit with few key differences.

Development and Functional Testing of an Unloading Concept for Knee Osteoarthritis Patients: A Pilot Study.

This paper presents a knee brace design that applies an extension moment to unload the muscles in stance phase during gait, and thereby the knee, as alternative to conventional valgus braces for knee osteoarthritis patients. The concept was tested on one healthy subject during normal gait with a prototype, which was designed to activate and deactivate in order to apply the extension moment in the stance phase only and hereby avoid any interference during the swing phase. Electromyography measurements and musculoskeletal models were used to evaluate the brace effects on muscle activation and knee compressive forces, respectively. Simulations predicted an ideal reduction of up to 36%, whereas experimental tests revealed a reduction of up to 24% with the current prototype. The prototype brace also reduced the knee joint force impulse up to 9% and electromyography (EMG) peak signal of the vasti muscles with up to 19%. Due to these reductions on a healthy subject, this bracing approach seems promising for reducing knee loads during normal gait. However, further clinical experiments on knee osteoarthritis patients are required to evaluate the effect on both pain and disease progression.

Effect of Ligament Properties on Nonlinear Dynamics and Wear Prediction of Knee Prostheses.

Although wear is known as the primary cause of long-time failure of total knee arthroplasty (TKA), it can be vital in short- and midterm TKA failure due to laxity. One of the reasons leading to joint laxity and instability is ligamentous insufficiency. This study, therefore, aims to investigate the effects of insufficient ligaments-related knee laxity on both nonlinear dynamics and wear of TKA. The study hypothesizes (a) ligamentous insufficiency can increase TKA damage; (b) stiffness reduction of each of the posterior cruciate ligament (PCL) and medial-lateral collateral ligaments (MCL-LCL) can differently contribute to TKA damage. A forward dynamics methodology is developed and the ligament behavior is simulated employing an asymmetric nonlinear elastic model. External loads and moment, due to the presence of all soft tissues, e.g., muscles and hip joint reaction forces, applied to the femoral bone are determined using a musculoskeletal approach linked to the developed model. A mesh density analysis is performed and comparing outcomes with that available in the literature allows for the assessment of our approach. From the results acquired, reduced PCL stiffness leads to an increase in linear wear rates and results in the maximum damage in TKAs. However, the maximum linear wear rates on both condyles occur once the stiffness of all ligaments is reduced. Moreover, the worn area of the tibia surface increases with the reduction in MCL-LCL stiffness on the medial condyle. The joint with insufficient PCL also shows a considerable increase in ligament forces right after toe-off.

Estimation of the Knee Adduction Moment and Joint Contact Force during Daily Living Activities Using Inertial Motion Capture.

Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed to estimate the knee adduction moment and tibiofemoral joint contact force during daily living activities using a musculoskeletal model with inertial motion capture derived kinematics in an elderly population. Eight elderly participants were instrumented with 17 inertial measurement units, as well as 53 opto-reflective markers affixed to anatomical landmarks. Participants performed stair ascent, stair descent, and sit-to-stand movements while both motion capture methods were synchronously recorded. A musculoskeletal model containing 39 degrees-of-freedom was used to estimate the knee adduction moment and tibiofemoral joint contact force. Strong to excellent Pearson correlation coefficients were found for the IMC-derived kinematics across the daily living tasks with root mean square errors (RMSE) between 3° and 7°. Furthermore, moderate to strong Pearson correlation coefficients were found in the knee adduction moment and tibiofemoral joint contact forces with RMSE between 0.006⁻0.014 body weight × body height and 0.4 to 1 body weights, respectively. These findings demonstrate that inertial motion capture may be used to estimate knee adduction moments and tibiofemoral contact forces with comparable accuracy to optical motion capture.

Evaluation of a Surrogate Contact Model in Force-Dependent Kinematic Simulations of Total Knee Replacement.

Knowing the forces in the human body is of great clinical interest and musculoskeletal (MS) models are the most commonly used tool to estimate them in vivo. Unfortunately, the process of computing muscle, joint contact, and ligament forces simultaneously is computationally highly demanding. The goal of this study was to develop a fast surrogate model of the tibiofemoral (TF) contact in a total knee replacement (TKR) model and apply it to force-dependent kinematic (FDK) simulations of activities of daily living (ADLs). Multiple domains were populated with sample points from the reference TKR contact model, based on reference simulations and design-of-experiments. Artificial neural networks (ANN) learned the relationship between TF pose and loads from the medial and lateral sides of the TKR implant. Normal and right-turn gait, rising-from-a-chair, and a squat were simulated using both surrogate and reference contact models. Compared to the reference contact model, the surrogate contact model predicted TF forces with a root-mean-square error (RMSE) lower than 10 N and TF moments lower than 0.3 N·m over all simulated activities. Secondary knee kinematics were predicted with RMSE lower than 0.2 mm and 0.2 deg. Simulations that used the surrogate contact model ran on average three times faster than those using the reference model, allowing the simulation of a full gait cycle in 4.5 min. This modeling approach proved fast and accurate enough to perform extensive parametric analyses, such as simulating subject-specific variations and surgical-related factors in TKR.

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture.

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.

A subject-specific musculoskeletal modeling framework to predict in vivo mechanics of total knee arthroplasty.

Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth "grand challenge competition to predict in vivo knee loads" dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R2) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > -0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > -0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

Sensitivity of knee cartilage biomechanics in finite element analysis to selected Musculoskeletal models.

Knee joint kinematics and kinetics analyzed by musculoskeletal (MS) modeling are often utilized in finite element (FE) models, estimating tissue-level mechanical responses. We compared knee cartilage stresses, strains, and centers of pressure of FE models driven by two widely used MS models, implemented in AnyBody and OpenSim. Minor discrepancies in the results were observed between the models. AnyBody-driven FE models showed slightly higher stresses in the medial tibial cartilage, while OpenSim-driven FE models estimated more anterior and lateral center of pressure. Recognizing these differences in the MS-FE models is important to ensure reliable analysis of cartilage mechanics and failure and simulation of rehabilitation.

Effects of gait modifications on tissue-level knee mechanics in individuals with medial tibiofemoral osteoarthritis: A proof-of-concept study towards personalized interventions.

Gait modification is a common nonsurgical approach to alter the mediolateral distribution of knee contact forces, intending to decelerate or postpone the progression of mechanically induced knee osteoarthritis (KOA). Nevertheless, the success rate of these approaches is controversial, with no studies conducted to assess alterations in tissue-level knee mechanics governing cartilage degradation response in KOA patients undertaking gait modifications. Thus, here we investigated the effect of different conventional gait conditions and modifications on tissue-level knee mechanics previously suggested as indicators of collagen network damage, cell death, and loss of proteoglycans in knee cartilage. Five participants with medial KOA were recruited and musculoskeletal finite element analyses were conducted to estimate subject-specific tissue mechanics of knee cartilages during two gait conditions (i.e., barefoot and shod) and six gait modifications (i.e., 0°, 5°, and 10° lateral wedge insoles, toe-in, toe-out, and wide stance). Based on our results, the optimal gait modification varied across the participants. Overall, toe-in, toe-out, and wide stance showed the greatest reduction in tissue mechanics within medial tibial and femoral cartilages. Gait modifications could effectually alter maximum principal stress (~20 ± 7%) and shear strain (~9 ± 4%) within the medial tibial cartilage. Nevertheless, lateral wedge insoles did not reduce joint- and tissue-level mechanics considerably. Significance: This proof-of-concept study emphasizes the importance of the personalized design of gait modifications to account for biomechanical risk factors associated with cartilage degradation.

Influence of gravity on biomechanics in flywheel squat and leg press.

Resistance exercise on Earth commonly involves both body weight and external load. When developing exercise routines and devices for use in space, the absence of body weight is not always adequately considered. This study compared musculoskeletal load distribution during two flywheel resistance knee-extension exercises, performed in the direction of (vertical squat; S) or perpendicular to (horizontal leg press; LP) the gravity vector. Eleven participants performed these two exercises at a given submaximal load. Motion analysis and musculoskeletal modelling were used to compute joint loads and to simulate a weightless situation. The flywheel load was more than twice as high in LP as in S (p < 0.001). Joint moments and forces were greater during LP than during S in the ankle, hip and lower back (p < 0.01) but were similar in the knee. In the simulated weightless situation, hip and lower-back loadings in S were higher than corresponding values at Earth gravity (p ≤ 0.01), whereas LP joint loads did not increase. The results suggest that LP is a better terrestrial analogue than S for knee-extension exercise in weightlessness and that the magnitude and direction of gravity during resistance exercise should be considered when designing and evaluating countermeasure exercise routines and devices for space.

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.

Computational modeling of a forward lunge: towards a better understanding of the function of the cruciate ligaments.

This study investigated the function of the cruciate ligaments during a forward lunge movement. The mechanical roles of the anterior and posterior cruciate ligament (ACL, PCL) during sagittal plane movements, such as forward lunging, are unclear. A forward lunge movement contains a knee joint flexion and extension that is controlled by the quadriceps muscle. The contraction of the quadriceps can cause anterior tibial translation, which may strain the ACL at knee joint positions close to full extension. However, recent findings suggest that it is the PCL rather than the ACL which is strained during forward lunging. Thus, the purpose of the present study was to establish a musculoskeletal model of the forward lunge to computationally investigate the complete mechanical force equilibrium of the tibia during the movement to examine the loading pattern of the cruciate ligaments. A healthy female was selected from a group of healthy subjects who all performed a forward lunge on a force platform, targeting a knee flexion angle of 90°. Skin-markers were placed on anatomical landmarks on the subject and the movement was recorded by five video cameras. The three-dimensional kinematic data describing the forward lunge movement were extracted and used to develop a biomechanical model of the lunge movement. The model comprised two legs including femur, crus, rigid foot segments and the pelvis. Each leg had 35 independent muscle units, which were recruited according to a minimum fatigue criterion. This approach allowed a full understanding of the mechanical equilibrium of the knee joint, which revealed that the PCL had an important stabilizing role in the forward lunge movement. In contrast, the ACL did not have any significant mechanical function during the lunge movement. Furthermore, the results showed that m. gluteus maximus may play a role as a knee stabilizer in addition to the hamstring muscles.

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

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.

BACKGROUND: 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. RESEARCH QUESTION: 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? METHODS: 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. RESULTS: 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. SIGNIFICANCE: 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.

Comparison of Joint and Muscle Biomechanics in Maximal Flywheel Squat and Leg Press.

The aim was to compare the musculoskeletal load distribution and muscle activity in two types of maximal flywheel leg-extension resistance exercises: horizontal leg press, during which the entire load is external, and squat, during which part of the load comprises the body weight. Nine healthy adult habitually strength-training individuals were investigated. Motion analysis and inverse dynamics-based musculoskeletal modelling were used to compute joint loads, muscle forces, and muscle activities. Total exercise load (resultant ground reaction force; rGRF) and the knee-extension net joint moment (NJM) were slightly and considerably greater, respectively, in squat than in leg press (p ≤ 0.04), whereas the hip-extension NJM was moderately greater in leg press than in squat (p = 0.03). Leg press was performed at 11° deeper knee-flexion angle than squat (p = 0.01). Quadriceps muscle activity was similar in squat and leg press. Both exercise modalities showed slightly to moderately greater force in the vastii muscles during the eccentric than concentric phase of a repetition (p ≤ 0.05), indicating eccentric overload. That the quadriceps muscle activity was similar in squat and leg press, while rGRF and NJM about the knee were greater in squat than leg press, may, together with the finding of a propensity to perform leg press at deeper knee angle than squat, suggest that leg press is the preferable leg-extension resistance exercise, both from a training efficacy and injury risk perspective.

Comparison of predicted kinetic variables between Parkinson's disease patients and healthy age-matched control using a depth sensor-driven full-body musculoskeletal model.

BACKGROUND: Abnormalities in gait kinetics in patients with Parkinson's disease (PD) who have suffer from gait impairment have been noted using a conventional inverse dynamic analysis derived by marker-based motion capture system and force plate, which are typically mounted in the laboratory floor. Despite the high accuracy of this approach in tracking markers' trajectories and acquiring ground reaction forces (GRFs), its dependence on laboratory-mounted equipment restricts its potential use in wider variety of clinical applications. RESEARCH QUESTION: Would a full-body musculoskeletal model driven by a single depth sensor data only produce comparable gait kinetic parameters, including GRFs and lower extremity joints moments, for elderly participants, both healthy and those diagnosed with PD? METHODS: Nine patients diagnosed with PD and 11 healthy age-matched control participants performed three over-ground gait trials. Full-body kinematic data were collected using a depth sensor and a musculoskeletal model have been constructed using AnyBody musculoskeletal modeling system to predict the three-dimensional GRFs and lower extremity joint moments. Predicted kinetic parameters for both PD and control groups were compared during the braking and propulsive phases of the gait cycle. In addition, ensemble curve analysis with 90% confidence intervals were constructed to compare between group differences across the stance phase of the gait cycle. RESULTS: The findings of this study showed that the PD exhibited a significantly lower braking peak vertical GRF and propulsion peak horizontal GRF while no significant between-group differences were found in peak lower extremity joint moments. However, the PD showed significant alterations in lower extremity joint moments during the early and late phases of stance, which indicate a difference in ambulation strategy. SIGNIFICANCE: The proposed method adopting full-body musculoskeletal model driven by a depth sensor data proves that it has the potential to be a portable and cost-effective gait analysis tool in the clinical setting.

A closed-form formulation for the conformal articulation of metal-on-polyethylene hip prostheses: Contact mechanics and sliding distance.

Using Hertz contact law results in inaccurate outcomes when applied to the soft conformal hip implants. The finite element method also involves huge computational time and power. In addition, the sliding distance computed using the Euler rotation method does not incorporate tribology of bearing surfaces, contact mechanics and inertia forces. This study, therefore, aimed to develop a nonlinear dynamic model based on the multibody dynamic methodology to predict contact pressure and sliding distance of metal-on-polyethylene hip prosthesis, simultaneously, under normal walking condition. A closed-form formulation of the contact stresses distributed over the articulating surfaces was derived based upon the elastic foundation model, which reduced computational time and cost significantly. Three-dimensional physiological loading and motions, inertia forces due to hip motion and energy loss during contact were incorporated to obtain contact properties and sliding distance. Comparing the outcomes with that available in the literature and a finite element analysis allowed for the validation of our approach. Contours of contact stresses and accumulated sliding distances at different instants of the walking gait cycle were investigated and discussed. It was shown that the contact point at each instant was located within the zone with the corresponding highest accumulated sliding distance. In addition, the maximum contact pressure and area took place at the stance phase with a single support. The stress distribution onto the cup surface also conformed to the contact point trajectory and the physiological loading.

Prediction of ground reaction forces for Parkinson's disease patients using a kinect-driven musculoskeletal gait analysis model.

Kinetic gait abnormalities result in reduced mobility among individuals with Parkinson's disease (PD). Currently, the assessment of gait kinetics can only be achieved using costly force plates, which makes it difficult to implement in most clinical settings. The Microsoft Kinect v2 has been shown to be a feasible clinic-based alternative to more sophisticated three-dimensional motion analysis systems in producing acceptable spatiotemporal and kinematic gait parameters. In this study, we aimed to validate a Kinect-driven musculoskeletal model using the AnyBody modeling system to predict three-dimensional ground reaction forces (GRFs) during gait in patients with PD. Nine patients with PD performed over-ground walking trials as their kinematics and ground reaction forces were measured using a Kinect v2 and force plates, respectively. Kinect v2 model-based and force-plate measured peak vertical and horizontal ground reaction forces and impulses produced during the braking and propulsive phases of the gait cycle were compared. Additionally, comparison of ensemble curves and associated 90% confidence intervals (CI90) of the three-dimensional GRFs were constructed to investigate if the Kinect sensor could provide consistent and accurate GRF predictions throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for predicting GRFs produced during gait for patients with PD. However, the observed findings should be replicated and model reliability established prior to integration into the clinical setting.