Ground Reaction Force Measurements Research Articles

Comparing the Ground Reaction Forces, Toe Clearances, and Stride Lengths of Young and Older Adults Using a Novel Shoe Sensor System

In this study, we developed a lightweight shoe sensor system equipped with four high-capacity, compact triaxial force sensors and an inertial measurement unit. Remarkably, this system enabled measurements of localized three-directional ground reaction forces (GRFs) at each sensor position (heel, first and fifth metatarsal heads, and toe) and estimations of stride length and toe clearance during walking. Compared to conventional optical motion analysis systems, the developed sensor system provided relatively accurate results for stride length and minimum toe clearance. To test the performance of the system, 15 older and 8 young adults were instructed to walk along a straight line while wearing the system. The results reveal that compared to the young adults, older adults exhibited lower localized GRF contributions from the heel and greater localized GRF contribution from the toe and fifth metatarsal locations. Furthermore, the older adults exhibited greater variability in their stride length and smaller toe clearance with greater variability compared to the young adults. These results underscore the effectiveness of the proposed gait analysis system in distinguishing the gait characteristics of young and older adults, potentially replacing traditional motion capture systems and force plates in gait analysis.

A split-belt instrumented treadmill with uneven terrain

The biomechanics of walking are far less understood for uneven terrain than flat or even surfaces. This is due in part to a lack of ground reaction force and moment recordings from each leg. These are often obtained with split-belt instrumented treadmills, which are currently incompatible with uneven terrain, making it difficult to perform biomechanics analyses such as inverse dynamics. Here we show how a standard split-belt instrumented treadmill (Bertec, Inc., Columbus, OH) can be modified to accommodate a variety of uneven terrains. The principal design considerations are structural clearance to allow passage of an uneven treadmill belt and fabrication of the terrain. We designed mechanical components with sufficient clearance for terrains up to 0.045 m high, and formed the terrain from uneven strips of polystyrene. Measured ground reaction forces from each leg at typical walking speeds agreed well with an intact benchmark treadmill (minimum interclass cross correlation score = 0.97). The modifications had negligible effect on the treadmill’s structural strength. The terrain produced some noise-like vibrations, but at much higher frequencies than fundamental to human locomotion. The uneven terrain treadmill can record many steps of the full complement of ground reaction forces and moments from individual legs.

The Variable Stiffness Treadmill 2: Development and Validation of a Unique Tool to Investigate Locomotion on Compliant Terrains

Abstract Understanding legged locomotion in various environments is valuable for many fields, including robotics, biomechanics, rehabilitation, and motor control. Specifically, investigating legged locomotion in compliant terrains has recently been gaining interest for the robust control of legged robots over natural environments. At the same time, the importance of ground compliance has also been highlighted in poststroke gait rehabilitation. Currently, there are not many ways to investigate walking surfaces of varying stiffness. This article introduces the variable stiffness treadmill (VST) 2, an improvement of the first version of the VST, which was the first treadmill able to vary belt stiffness. In contrast to the VST 1, the device presented in this paper (VST 2) can reduce the stiffness of both belts independently, by generating vertical deflection instead of angular, while increasing the walking surface area from 0.20m2 to 0.74m2. In addition, both treadmill belts are now driven independently, while high-spatial-resolution force sensors under each belt allow for measurement of ground reaction forces and center of pressure. Through validation experiments, the VST 2 displays high accuracy and precision. The VST 2 has a stiffness range of 13kN/m to 1.5MN/m, error of less than 1%, and standard deviations of less than 2.2kN/m, demonstrating its ability to simulate low-stiffness environments reliably. The VST 2 constitutes a drastic improvement of the VST platform, a one-of-its-kind system that can improve our understanding of human and robotic gait while creating new avenues of research on biped locomotion, athletic training, and rehabilitation of gait after injury or disease.

Bilateral hip stability variation in the functional ambulation and kinetic parameters after total hip arthroplasty during leveled walking.

This study determines gender variation, comparing the significance level between men and women related to functional ambulation characteristics after hip arthroplasty. The study focuses on the broader female pelvis and how it affects the rehabilitation regimen following total hip arthroplasty. In this cross-sectional study, 20 cases of right hip arthroplasty were divided into 10 male and 10 female cases, aged 40-65years. The functional ambulation parameters (walking cadence, gait speed, stride length, and gait cycle time) were acquired from the GAITRite device, as well as kinematic values for hip frontal plane displacement and kinetic parameters for ground response force in the medial-lateral direction. An independent t-test showed a significant difference in the kinematic parameter variables for the anterior superior iliac spine, more significant trochanter displacement, and hip abduction angle between the operated and non-operated limbs for each group separately. Regarding the functional ambulation parameters, there was a significant difference in the walking cadence between the operated and non-operated limbs of both male and female groups. Moreover, the output variables of ground reaction force measures revealed significant differences between their operated and non-operated limbs. The linear regression model used was consistent with the current results, demonstrating a weak negative correlation between the abduction angle of the operated hip and gait speed for both male and female groups. Based on the findings, we draw the conclusion that improving a rehabilitated physical therapy program for the abductors of both male and female patients' operated and non-operated limbs is essential for normalizing the ground reaction force value, avoiding focus on the operated hip, and reducing the amount of time that the operated hip's abductors must perform. This involves exposing the surgically repaired limb to the risk of post-operative displacement or dislocation, particularly in female patients.

Development of an Ankle Sensor for Ground Reaction Force Measurement in Intelligent Prosthesis

Development of an Ankle Sensor for Ground Reaction Force Measurement in Intelligent Prosthesis

Effects of the intermittent theta burst stimulation on gait, balance and lower limbs motor function in stroke: study protocol for a double-blind randomised controlled trial with multimodal neuroimaging assessments

IntroductionApproximately, 50% of stroke survivors experience impaired walking ability 6 months after conventional rehabilitation and standard care. However, compared with upper limb motor function, research on lower limbs rehabilitation through...

Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae

During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1–7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These ‘protopodia’ form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.

Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae.

During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1-7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These 'protopodia' form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.

Relationships Between Force-Time Curve Variables and Tennis Serve Performance in Competitive Tennis Players.

Fourel, L, Touzard, P, Fadier, M, Arles, L, Deghaies, K, Ozan, S, and Martin, C. Relationships between force-time curve variables and tennis serve performance in competitive tennis players. J Strength Cond Res 38(9): 1667-1674, 2024-Practitioners consider the role of the legs in the game of tennis as fundamental to achieve high performance. But, the exact link between leg actions and high-speed and accurate serves still lacks understanding. Here, we investigate the correlation between force-time curve variables during serve leg drive and serve performance indicators. Thirty-six competitive players performed fast serves, on 2 force plates, to measure ground reaction forces (GRF). Correlation coefficients describe the relationships between maximal racket head velocity, impact height, and force-time curve variables. Among all the variables tested, the elapsed time between the instants of maximal vertical and maximal anteroposterior GRF ( r = -0.519, p < 0.001) and the elapsed time between the instant of maximal anteroposterior GRF and ball impact ( r = -0.522, p < 0.001) are the best predictors of maximal racket velocity. Maximal racket head velocity did not significantly correlate with the mean or maximal vertical GRF or with the mean or maximum rate of vertical force development. The best predictor for impact height is the relative net vertical impulse during the concentric phase ( r = 0.772, p < 0.001). This work contributes to a better understanding of the mechanical demands of tennis serve motion and gives guidelines to improve players preparation and performance. Trainers should encourage their players to better synchronize their upward and forward pushing action during the serve to increase maximal racket head velocity. Players should also aim to improve their relative net vertical impulse to increase impact height through strength training and technical instructions.

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.

Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

Wearable Robot Design Optimization Using Closed-Form Human-Robot Dynamic Interaction Model.

Wearable robots are emerging as a viable and effective solution for assisting and enabling people who suffer from balance and mobility disorders. Virtual prototyping is a powerful tool to design robots, preventing the costly iterative physical prototyping and testing. Design of wearable robots through modelling, however, often involves computationally expensive and error-prone multi-body simulations wrapped in an optimization framework to simulate human-robot-environment interactions. This paper proposes a framework to make the human-robot link segment system statically determinate, allowing for the closed-form inverse dynamics formulation of the link-segment model to be solved directly in order to simulate human-robot dynamic interactions. The paper also uses a technique developed by the authors to estimate the walking ground reactions from reference kinematic data, avoiding the need to measure them. The proposed framework is (a) computationally efficient and (b) transparent and easy to interpret, and (c) eliminates the need for optimization, detailed musculoskeletal modelling and measuring ground reaction forces for normal walking simulations. It is used to optimise the position of hip and ankle joints and the actuator torque-velocity requirements for a seven segments of a lower-limb wearable robot that is attached to the user at the shoes and pelvis. Gait measurements were carried out on six healthy subjects, and the data were used for design optimization and validation. The new technique promises to offer a significant advance in the way in which wearable robots can be designed.

Enhancing force plate design through optimization of spring constant distribution

Force plates have been widely used in various scientific fields to measure ground reaction forces experienced by animals and humans. A force plate that incorporates mechanical springs and non-contact displacement sensors offers the advantages of facilely modifying its shape and performance. However, the design protocol was limited to adjusting the plate size and spring constants of the springs supporting the plate at the four corners. Herein, we propose an optimization method for determining the spring constant distribution of a force plate. The proposed design enables the use of simulations to optimize the mechanical properties of the force plate, such as resonant frequency and error due to position. The experimental results demonstrated that the resonant frequency of the fabricated force plate was 133 Hz and the error due to position was within ±5 %. These results confirm that the proposed force plate can be effectively adjusted through optimization.

Running Biomechanics and Clinical Features Among Adolescent Athletes With Lower Leg Chronic Exertional Compartment Syndrome.

To compare clinical measures between patients with chronic exertional compartment syndrome (CECS) and healthy controls and evaluate running biomechanics, physical measurements, and exertional intracompartmental (ICP) changes in adolescent athletes with lower leg CECS. Cross-sectional case-control study. Large tertiary care hospital and affiliated injury prevention center. Forty-nine adolescents with CECS (39 F, 10 M; age: 16.9 ± 0.8 years; body mass index (BMI): 23.1 ± 2.9 kg/m 2 ; symptom duration: 8 ± 12 months) were compared with 49 healthy controls (39 F, 10 M; age: 6.9 ± 0.8 years; BMI: 20.4 ± 3.7 kg/m 2 ). All participants underwent gait analyses on a force plate treadmill and clinical lower extremity strength and range of motion testing. Patients with chronic exertional compartment syndrome underwent Stryker monitor ICP testing. Symptoms, menstrual history, and ICP pressures of the patients with CECS using descriptive statistics. Mann-Whitney U and χ 2 analyses were used to compare CECS with healthy patients for demographics, clinical measures, and gait biomechanics continuous and categorical outcomes, respectively. For patients with CECS, multiple linear regressions analyses were used to assess associations between gait biomechanics, lower extremity strength and range of motion, and with ICP measures. The CECS group demonstrated higher mass-normalized peak ground reaction force measures (xBW) compared with controls (0.21 ± 0.05 xBW ( P < 0.001) and were more likely to have impact peak at initial contact ( P = 0.04). Menstrual dysfunction was independently associated with higher postexertion ICP (ß = 14.6; P = 0.02). The CECS group demonstrated increased total force magnitude and vertical impact transient peaks. In women with CECS, menstrual dysfunction was independently associated with increased postexertion ICP. These biomechanical and physiological attributes may play a role in the development of CECS.

MEMS highly sensitive and large-area force plate for total ground reaction force measurement of running ant

Terrestrial insects exhibit agile manoeuvrability while running. However, the ground reaction force (GRF) in small insects remains poorly understood owing to its size and acting force. Here, we present a force plate for the measurement of total GRF during the running motion of ants with a force on the order of several tens of μN. The proposed force plate consists of a sufficiently large plate for several step cycles and four supporting cantilevers with highly sensitive piezoresistive elements. The plate and sensor chips were fabricated separately and combined during postprocessing. Two sizes of force plates were designed and fabricated for a large ant (Camponotus japonicas) and small ant (Messor aciculatus) with force resolution less than 1 μN. The developed force plates were calibrated by applying vertical forces at 32 points on the plate. Using the fabricated force plate, we measured the total GRF over several step cycles as the ant ran along the plate. Consequently, it was suggested that the ant ran with small vibrations in the direction of gravity.

Acute effects of caffeine supplementation on kinematics and kinetics of sprinting.

We investigated the acute effects of caffeine supplementation (6 mg･kg-1 ) on 60-m sprint performance and underlying components with a step-to-step ground reaction force measurement in 13 male sprinters. After the first round sprint as a control, caffeine supplementation-induced improvement in 60-m sprint times (7.811 s at the first versus 7.648 s at the second round, 2.05%) were greater compared with the placebo condition (7.769 s at the first versus 7.768 s at the second round, 0.02%). Using average values for every four steps, in the caffeine condition, higher running speed (all six step groups), higher step frequency (5th-16th and 21st-24th step groups), shorter support time (all the step groups except for 13th-16th step) and shorter braking time (9th-24th step groups) were found. Regarding ground reaction forces variables, greater braking mean force (13th-19th step group), propulsive mean force (1st-12th and 17th-20th step groups), and effective vertical mean force (9th-12th step group) were found in the caffeine condition. For the block clearance phase at the sprint start, push-off and reaction times did not change, while higher total anteroposterior mean force, average horizontal external power, and ratio of force were found in the caffeine condition. These results indicate that, compared with placebo, acute caffeine supplementation improved sprint performance regardless of sprint sections during the entire acceleration phase from the start through increases in step frequency with decreases in support time. Moreover, acute caffeine supplementation promoted increases in the propulsive mean force, resulting in the improvement of sprint performance.

Absorption function loss due to the history of previous ankle sprain explored by unsupervised machine learning

BackgroundAnkle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. PurposeTo identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. MethodsEighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. FindingsThe ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. SignificancePCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.

Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation

Several studies have reported methods for capturing ground reaction forces in the field. However, these forces are usually measured indirectly with non-interpretable “black box” models. Here we report an interpretable model with a function approximation algorithm. The model uses time series of plantar pressure from instrumented insoles to estimate vertical ground reaction forces. The study included data from 16 persons moving at different speeds on a two-belt treadmill equipped with force plates. The introduced regression model, based on a high-dimensional approximation, using only low-dimensional variable interactions, demonstrated its ability to estimate vertical ground reaction forces. The normalized mean square error for velocities between 3 and 9kmh-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9\\,\\hbox {km}\\,\\hbox {h}^{-1}$$\\end{document} ranged from 10.6 to 24.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$24.4\\,\\%$$\\end{document}. The accuracy of the presented approach, which can be used to analyze and interpret the learned model, was comparable to that reported in the literature. Furthermore, the evaluation of the learned model is particularly suitable for embedded and portable systems and, after a one-time calibration measurement, allows permanent and laboratory-independent measurement of vertical ground reaction forces.

Ground reaction force as a factor responsible for the topography of injuries in professional dance. An analysis of three dance styles: classical dance, modern dance, and folk dance.

The study aimed to identify the effects of ground reaction forces (GRF) recorded during landing in typical elements of three dance styles, including classical, modern, and folk dance, on injuries` topography. The research involved a survey and measurements of GRF generated during landing after the jump. The survey involved a group of 90 professional dancers. In the questionnaire, the dancers marked areas of the human body that were affected at least once by injuries. Biomechanical tests of the GRF recording were conducted on a group of 15 professional dancers. The analysis focused on the following parameters: a maximum value of the vertical variable of the GRF relative to body weight (maxGRFz), the time between the moment from first foot contact with the ground to the moment of reaching the maxGRFz (tmaxGRFz), and the loading rate of the GRF relative to body weight (LRGRFz). Regardless of dance style and sex, the lower spine, knee joints, ankle joints and feet were the areas most affected by injuries among professional dancers. The level of maxGRFz, tmaxGRFz and LRGRFz during typical jumps in classical, modern, and folk dance was statistically significantly different (P<0.01*). The highest mean maxGRFz values were recorded for jumps performed by classical dancers. Furthermore, the sum of injury-affected areas differed significantly across various dance styles and was connected with the impact forces transferred by the dancer's musculoskeletal system. The level of GRF is one of the decisive factors affecting the topography of professional dance injuries.

Gait Sensing and Haptic Feedback Using an Inflatable Soft Haptic Sensor

Abstract Collecting gait data and providing haptic feedback are essential for the safety and efficiency of robot-based rehabilitation. However, readily available devices that can perform both are scarce. This work presents a novel method for mutual sensing and haptic feedback, through the development of an inflatable soft haptic sensor (ISHASE). The design, modeling, and characterization of ISHASE are discussed. Four ISHASEs are embedded in the insole of a shoe to measure ground reaction forces and provide haptic feedback. Four participants were recruited to evaluate the performance of ISHASE as a sensor and haptic device. Experimental results indicate that ISHASE can accurately estimate user’s ground reaction forces while walking, with a maximum and a minimum accuracy of 91% and 85%, respectively. Haptic feedback was delivered to four different locations under the foot, and users could identify the location with an average 92% accuracy. A case study that exemplifies a rehabilitation scenario is presented to demonstrate ISHASE’s usefulness for mutual sensing and haptic feedback.

Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression

The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.