Prediction Of Ground Reaction Forces Research Articles

Calibrationless monocular vision musculoskeletal simulation during gait

With computer vision technology and prediction of ground reaction forces (GRF), a previous study performed markerless motion capture and musculoskeletal simulation with two smartphones (OpenCap). A recent approach can reconstruct 3D human motion from a single video without calibration and it may further simplify the motion capture process. However it has not been combined with musculoskeletal simulation and the validity is unclear. Therefore, the purpose of this study was to determine the validity of the musculoskeletal simulation using a monocular vision approach. An open-source dataset that contains motion capture and video data during gait from 10 healthy participants was used. Human motion reconstruction with the skinned human (SMPL) model was performed on each video. Virtual marker data was generated by extracting the position data from the SMPL skin vertices. Inverse kinematics, GRF prediction (only for monocular vision approach), inverse dynamics and static optimization were performed using a musculoskeletal model for experimental motion capture data and the generated virtual markers from videos. Mean absolute errors (MAE) between motion capture based and monocular vision based simulation outcomes were calculated. The MAE were 8.4° for joint angles, 5.0 % bodyweight for GRF, 1.1 % bodyweight*height for joint moments and 0.11 for estimated muscle activations from 16 muscles. The entire MAE was larger but some were comparable to OpenCap. Using the monocular vision approach, motion capture and musculoskeletal simulation can be done with no preparations and is beneficial for clinicians to quantify the daily gait assessment.

Validity of muscle activation estimated with predicted ground reaction force in inverse dynamics based musculoskeletal simulation during gait

The inverse dynamics based musculoskeletal simulation needs ground reaction forces (GRF) as an external force input. GRF can be predicted from kinematic data. However, the validity of estimated muscle activation using the predicted GRF has remained unclear. Therefore, the purpose of this study was to determine the validity of estimated muscle activation with predicted GRF in the inverse dynamics based musculoskeletal simulation. To perform musculoskeletal simulations, an open-source motion capture dataset that contains gait data from 50 healthy subjects was used. CusToM was used for the musculoskeletal simulations. Two sets of inverse dynamics and static optimization were performed, one used predicted GRF (PRED) and another used experimentally measured GRF (EXP). Pearson’s correlation was calculated to evaluate the similarity between EMG and estimated muscle activations for both PRED and EXP. To compare PRED and EXP, paired t-tests were used to compare the trial-wise muscle activation similarity and residuals. Relationships between joint moments and residuals were also tested. The overall muscle activation similarity was comparable in PRED (R = 0.477) and EXP (R = 0.475). The residuals were 2–4 times higher in EXP compared to PRED (P < 0.001). The hip flexion–extension moment was correlated to sagittal plane residual moment (R = 0.467). The muscle activations estimated using predicted GRF were comparable to that with measured GRF in the inverse dynamics based musculoskeletal simulation. Prediction of GRF helps to perform musculoskeletal simulations where the force plates are not available.

The virtual pivot point concept improves predictions of ground reaction forces.

Ground reaction forces (GRFs) are essential for the analysis of human movement. To measure GRFs, 3D force plates that are fixed to the floor are used with large measuring ranges, excellent accuracy and high sample frequency. For less dynamic movements, like walking or squatting, portable 3D force plates are used, while if just the vertical component of the GRFs is of interest, pressure plates or in-shoe pressure measurements are often preferred. In many cases, however, it is impossible to measure 3D GRFs, e.g., during athletic competitions, at work or everyday life. It is still challenging to predict the horizontal components of the GRFs from kinematics using biomechanical models. The virtual pivot point (VPP) concept states that measured GRFs during walking intercept in a point located above the center of mass, while during running, the GRFs cross each other at a point below the center of mass. In the present study, this concept is used to compare predicted GRFs from measured kinematics with measured 3D-GRFs, not only during walking but also during more static movements like squatting and inline lunge. To predict the GRFs a full-body biomechanical model was used while gradually changing the positions of the VPP. It is shown that an optimal VPP improves the prediction of GRFs not only for walking but also for inline lunge and squats.

Prediction of ground reaction forces and moments and joint kinematics and kinetics by inertial measurement units using 3D forward dynamics model

Prediction of ground reaction forces and moments and joint kinematics and kinetics by inertial measurement units using 3D forward dynamics model

Prediction of ground reaction forces and moments during walking in children with cerebral palsy.

Gait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy. The method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements. The results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71-0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation. To conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait.

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Using Wearable Sensors to Estimate Vertical Ground Reaction Force Based on a Transformer

In this paper, we present a new method to estimate ground reaction forces (GRF) from wearable sensors for a variety of real-world situations. We address the drawbacks of using force plates with limited activity range and high cost in previous work. We use a transformer encoder as a feature extractor to extract temporal and spatial features from wearable sensors more efficiently. Using the Mean Absolute Percentage Error (MAPE) as the evaluation criterion, the experimental results show that the average error of the predicted values using the transformer as a feature extractor improved by 32% compared to the RNN architecture and by 25% compared to the LSTM architecture. Finally, we use Gate_MSE to solve the problem of a large peak error in GRF prediction. Meanwhile, this paper explores the effect of the number of wearable sensors or wearable modes on GRF prediction.

Prediction of ground reaction forces using the artificial neural network from capacitive self-sensing values of composite ankle springs for exo-robots

Exoskeleton robots, which are wearable robots that assist human movement, have recently been developed to augment the performance of the human body. In this study, the ankle spring of lower-limb exoskeleton robots was created from a foam core sandwich-structured composite (FCSC) to support the running movement by storing and releasing elastic energy and to predict kinetic data using the self-sensed capacitance data from the structure. Through finite element analysis, the deformation behaviors of the FCSC ankle spring were analyzed and compared with those of a typical fiber-reinforced plastic composite ankle spring. Using a universal test machine, the capacitive-based self-sensing capability of the FCSC ankle spring was examined under several loading rates and cyclic loading. Furthermore, running tests with the lower-limb exoskeleton robot equipped with the FCSC ankle spring were performed on a treadmill. Horizontal and vertical ground reaction forces (GRFs) and capacitance values were obtained from the force plate embedded in the treadmill and FCSC ankle spring, respectively, at various running speeds. These data were used to train an artificial neural network (ANN) model as input and output data. Consequently, the trained ANN model predicted the kinetic data using the capacitance values of the FCSC ankle spring without additional sensors.

Prediction of Ground Reaction Forces during Walking with Depth Camera Sensors via Recurrent Neural Network

Prediction of Ground Reaction Forces during Walking with Depth Camera Sensors via Recurrent Neural Network

Estimation of the ground reaction forces from a single video camera based on the spring-like center of mass dynamics of human walking

In clinical studies, the ground reaction forces (GRFs) during walking have found being highly useful. Therefore, the force sensing shoes with small sensors and estimation methods based on kinematics from motion capture systems or inertial measurement units were proposed. Recent studies demonstrated methods of extracting GRFs from whole-body joint kinematics, which requires a significant computational load. In this study, we propose a vertical and anterior-posterior GRFs estimation method using a single camera based on the dynamic relationship between the center of mass (CoM) and the GRFs in terms of spring mechanics. The estimation method consisted of two steps: the extraction of the vertical CoM from the video clip and the conversion of the CoM information into GRFs using a walking model. From the image of the greater trochanter that is positioned near the pelvic joint, the vertical CoM was extracted. This was done after removing the artifacts by pelvic rotation and postural change of lower limbs. The parameters of a compliant bipedal walking model were tuned to best match the CoM trajectory coupled with GRFs by spring mechanics. A video camera was used to record the walking trials of five healthy young participants from the side. The walking trials was conducted at three different speeds on the instrumented treadmill; each lasted one minute long. The GRF prediction errors were approximately 9–11%, with the best matching trials found to be at a self-selected gait speed. The prediction of anterior-posterior GRF components showed a more consistent match than the vertical GRF. The results demonstrated the possibility of marker-less kinetics prediction from video images incorporating the mechanical characteristics of the CoM.

Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data.

Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of for double support motions and for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.

Spring-loaded inverted pendulum modeling improves neural network estimation of ground reaction forces

Inertial-measurement-unit (IMU)-based wearable gait-monitoring systems provide kinematic information but kinetic information, such as ground reaction force (GRF) are often needed to assess gait symmetry and joint loading. Recent studies have reported methods for predicting GRFs from IMU measurement data by using artificial neural networks (ANNs). To obtain reliable predictions, the ANN requires a large number of measurement inputs at the cost of wearable convenience. Recognizing that the dynamic relationship between the center of mass (CoM) and GRF can be well represented by using spring mechanics, in this study we propose two GRF prediction methods based on the implementation of walking dynamics in a neural network. Method 1 takes inputs to the network that were CoM kinematics data and Method 2 employs forces approximated from CoM kinematics by applying spring mechanics. The gait data of seven young healthy subjects were collected at various walking speeds. Leave-one-subject-out cross-validation was performed with normalized root mean square error and r as quantitative measures of prediction performance. The vertical and anteroposterior (AP) GRFs obtained using both methods agreed well with the experimental data, but Method 2 yielded improved predictions of AP GRF compared to Method 1 (p = 0.005). These results imply that knowledge of the dynamic characteristics of walking, combined with a neural network, could enhance the efficiency and accuracy of GRF prediction and help resolve the tradeoff between information richness and wearable convenience of wearable technologies.

IMU sensors beneath walking surface for ground reaction force prediction in gait

Objective : Utilization of inertial measurement units (IMU) data for ground reaction force (GRF) prediction has been widely studied and documented when these sensors attach to the body segments. However, it was inconvenient and required people’s cooperation. A novel approach of the current study was setting IMU sensors mounted underneath the walking surface to measure footstep induced structural vibration. We aimed to conduct the force plate to validate the prediction accuracy of this approach. Methods : Fifteen hundred steps were recorded from five individuals. Twenty-four measured features from four IMU sensors were treated as inputs to the long short-term memory model for multidimensional GRF predictions. The GRF data from the force plate were considered as the ground truth for comparisons. The accuracy performance was determined by the normalized root mean square error (NRMSE) method. Results : The averaged NRMSE was 6.05%, 3.93%, and 4.37% for Fx, Fy, and Fz, respectively. Conclusion : The accuracy was comparable with IMU sensors attached to the body, particularly in the vertical direction. The current study demonstrated the feasibility of this approach and successfully predicted ground reaction force with high accuracy. Significance : The validation of IMU sensors mounted underneath the walking surface for GRF prediction provides an alternative method for biometrics in gait.

Prediction of ground reaction forces while walking in water.

Despite being a key concept in rehabilitation, controlling weight-bearing load while walking, following lower limb injury is very hard to achieve. Walking in water provides an opportunity to prescribe load for people who have pain, weakness or weight bearing restrictions related to stages of healing. The aim of this experimental study was to evaluate and validate regression models for predicting ground reaction forces while walking in water. One hundred and thirty seven individuals (24±5 years, 1.71±0.08 m and 68.7±12.5 kg) were randomly assigned to a regression group (n = 113) and a validation group (n = 24). Trials were performed at a randomly assigned water depth (0.75 to 1.35 m), and at a self-selected speed. Independent variables were: immersion ratio, velocity, body mass, and waist, thigh and leg circumferences. Stepwise regression was used for the prediction of ground reaction forces and validation included agreement and consistency statistical analyses. Data from a force plate were compared with predicted data from the created model in the validation group. Body mass, immersion ratio, and velocity independently predicted 95% of the vertical and resultant ground reaction force variability, while, together, velocity and thigh circumference explained 81% of antero-posterior ground reaction force variability. When tested against the data measured in validation samples, the models output resulted in statistically similar values, intraclass correlation coefficients ranging from 0.88 to 0.90 and standard errors of measurement, 11.8 to 42.3 N. The models introduced in this study showed good predictive performance in our evaluation procedures and may be considered valid in the prediction of vertical, antero-posterior and resultant ground reaction forces while walking in water. All predictive variables can be easily determined in clinical practice. Future studies should focus on the validation of these models in specific populations.

Prediction of ground reaction forces in level and incline/decline walking from a multistage analysis of plantar pressure data

Knowing the ground reaction forces (GRFs) during walking has various biomechanical applications in injury prevention, gait analysis, as well as prosthetic and footwear design. The current study presents a method for predicting the GRFs in level and incline/decline walking that may be used in various outdoor biomechanics studies geared towards the above applications. The method was developed to predict the complete set of GRFs at walking inclinations of 0°, ±5°, ±10°, ±15°, and ±20°. Plantar pressure insoles were used to obtain inclination-specific, linear regression models based on three periods of gait stance phase, and the model-determined GRFs were compared with those measured from a forceplate. The three periods were determined based on the observed shifting of load-bearing insole sensors from heel to forefoot during walking, i.e., heel-strike, midstance, and toe-off. Six subjects wearing minimalist shoes fitted with plantar pressure insoles containing 99 pressure sensors performed ten walking trials at each of the aforementioned inclinations on an adjustable ramp with an embedded forceplate. Data from contact of the instrumented shoes with the forceplate were used to create linear regressions to transform insole pressure data into a complete set of GRFs. The root mean square error (RMSE) over peak recorded values were on average 10%, 3%, 21% for level walking and 11%, 4%, 23% for ramp walking in the respective anteroposterior, vertical, and mediolateral directions. The multistage linear regression model developed in the current study may be an acceptable option for estimating GRFs during walking in various environments without the restraint of a forceplate.

Prediction of Ground Reaction Forces of Flexible Barbells using their Bar End Displacement

Prediction of Ground Reaction Forces of Flexible Barbells using their Bar End Displacement

Concurrent prediction of ground reaction forces and moments and tibiofemoral contact forces during walking using musculoskeletal modelling

Ground reaction forces and moments (GRFs and GRMs) measured from force plates in a gait laboratory are usually used as the input conditions to predict the knee joint forces and moments via musculoskeletal (MSK) multibody dynamics (MBD) model. However, the measurements of the GRFs and GRMs data rely on force plates and sometimes are limited by the difficulty in some patient's gait patterns (e.g. treadmill gait). In addition, the force plate calibration error may influence the prediction accuracy of the MSK model. In this study, a prediction method of the GRFs and GRMs based on elastic contact element was integrated into a subject-specific MSK MBD modelling framework of total knee arthroplasty (TKA), and the GRFs and GRMs and knee contact forces (KCFs) during walking were predicted simultaneously with reasonable accuracy. The ground reaction forces and moments were predicted with an average root mean square errors (RMSEs) of 0.021 body weight (BW), 0.014 BW and 0.089 BW in the antero–posterior, medio–lateral and vertical directions and 0.005 BW•body height (BH), 0.011 BW•BH, 0.004 BW•BH in the sagittal, frontal and transverse planes, respectively. Meanwhile, the medial, lateral and total tibiofemoral (TF) contact forces were predicted by the developed MSK model with RMSEs of 0.025–0.032 BW, 0.018–0.022 BW, and 0.089–0.132 BW, respectively. The accuracy of the predicted medial TF contact force was improved by 12% using the present method. The proposed method can extend the application of the MSK model of TKA and is valuable for understanding the in vivo knee biomechanics and tribological conditions without the force plate data.

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.

Prediction of ground reaction forces and moments during sports-related movements

When performing inverse dynamic analysis (IDA) of musculoskeletal models to study human motion, inaccuracies in experimental input data and a mismatch between the model and subject lead to dynamic inconsistency. By predicting the ground reaction forces and moments (GRF&Ms) this inconsistency can be reduced and force plate measurements become unnecessary. In this study, a method for predicting GRF&Ms was validated for an array of sports-related movements. The method was applied to ten healthy subjects performing, for example, running, a side-cut manoeuvre, and vertical jump. Pearson’s correlation coefficient ( $r$ ) and root-mean-square deviation were used to compare the predicted GRF&Ms and associated joint kinetics to the traditional IDA approach, where the GRF&Ms were measured using force plates. The main findings were that the method provided estimates comparable to traditional IDA across all movements for vertical GRFs ( $r$ ranging from 0.97 to 0.99, median 0.99), joint flexion moments ( $r$ ranging from 0.79 to 0.98, median 0.93), and resultant joint reaction forces ( $r$ ranging from 0.78 to 0.99, median 0.97). Considering these results, this method can be used instead of force plate measurements, hereby, facilitating IDA in sports science research and enabling complete IDA using motion analysis systems that do not incorporate force plate data.

A Nonlinear Leg Damping Model for the Prediction of Running Forces and Stability

Despite the neuromechanical complexity underlying animal locomotion, the steady-state center-of-mass motions and ground reaction forces of animal running can be predicted by simple spring-mass models such as the canonical spring-loaded inverted pendulum (SLIP) model. Such SLIP models have been useful for the fields of biomechanics and robotics in part because ground reaction forces are commonly measured and readily available for comparing with model predictions. To better predict the stability of running, beyond the canonical conservative SLIP model, more recent extensions have been proposed and investigated with hip actuation and linear leg damping (e.g., hip-actuated SLIP). So far, these attempts have gained improved prediction of the stability of locomotion but have led to a loss of the ability to accurately predict ground reaction forces. Unfortunately, the linear damping utilized in current models leads to an unrealistic prediction of damping force and ground reaction force with a large nonzero magnitude at touchdown (TD). Here, we develop a leg damping model that is bilinear in leg length and velocity in order to yield improved damping force and ground reaction force prediction. We compare the running ground reaction forces, small and large perturbation stability, parameter sensitivity, and energetic cost resulting from both the linear and bilinear damping models. We found that bilinear damping helps to produce more realistic, smooth vertical ground reaction forces, thus fixing the current problem with the linear damping model. Despite large changes in the damping force and power loss profile during the stance phase, the overall dynamics and energetics on a stride-to-stride basis of the two models are largely the same, implying that the integrated effect of damping over a stride is what matters most to the stability and energetics of running. Overall, this new model, an actuated SLIP model with bilinear damping, can provide significantly improved prediction of ground reaction forces as well as stability and energetics of locomotion.