Ground Reaction Force Measurements Research Articles

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.

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.

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.

Validation of a Portable Wireless Force Platform System To Measure Ground Reaction Forces During Various Tasks.

Force platforms are widely used in biomechanics to measure ground reaction forces (GRF) during various human movements. However, traditional force plates are not easily used outside a research lab. To overcome this issue, researchers and manufacturers are developing low-cost portable force platforms that can be used in a variety of settings, including outdoors. To validate the kinetic data obtained from a pair of portable K-Deltas force platforms compared to gold standard platforms fixed in the lab and to examine the measurement reliability between this pair of portable force platforms. Force-time curves from known masses, countermovement vertical jumps, and balance tests were used to assess validity of K-Deltas using a pair of Bertec force plates as a gold standard and between the K-Deltas pair of plates. Bland-Altman plots were used to evaluate the differences between K-Deltas and Bertec force plates. For the assessment of countermovement vertical jumps, impulse, peak rate of force development and peak force were calculated for both instruments and checked for agreement between instruments. Three young adults (2 male, 1 female, 25.4±0.83 years) participated in the study. The percentage of Bland-Altman plot point within the limits of agreement was 94.59 % for the comparison between K-Deltas and Bertec and 94.83% between the pair of K-Deltas. The results show that the portable force platforms could be utilized successfully for assessing pertinent parameters in clinical and sports biomechanics. The findings suggest that portable force platforms can be used as an alternative to traditional laboratory equipment for field assessment, providing significant improvements compared to the past. Level 3.

Wavelet Analysis of Rearfoot Loading of Patients After Medial Patellofemoral Ligament (MPFL) Reconstruction

Abstract Introduction. Patellar instability in younger patients is a common injury (3.3% of all knee injury episodes) and the most frequent dislocation of the knee observed. The sensitivity and reliability of clinical diagnostic tests after medial patellofemoral ligament (MPFL) reconstruction remain unclear and biased. The primary aim of the present study was thus to detect, using wavelet analysis, differences in the rearfoot loading of the operated limb of patients after MPFL reconstruction. The authors hypothesised that applying this specific examination technique would allow for more accurate and less biased clinical results evaluation. Material and Methods. The study involved a group of 15 female subjects (age: 22.8 ± 6.3 years; weight: 58.5 ± 5.1 kg) after MPFL reconstruction and a control group of 29 healthy females (23.2 ± 2.8 years; 61.1 ± 7.7 kg). The subjects performed a 30-second classic Romberg test on a treadmill (Noraxon FDN-T) equipped with a matrix of baroresistive sensors to measure ground reaction forces. Time-frequency analysis of the signal originating from the treadmill was done using an analytic Morse wavelet. Results. The results indicate differences in visual control and vestibular stability in patients before and 3 months after a surgery. They are characterised by a deficit of energy attributed to visual control in favour of an excess of energy associated with vestibular control. Patellar instability patients show greater energy expenditure to maintain body balance for at least 6 months after MPFL reconstruction. Conclusions. Patellar instability patients have increased vestibular control while maintaining balance. Our results suggest an extended rehabilitation period for patients after MPFL reconstruction beyond 6 months.

Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

ABSTRACT This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

Force plate coverings significantly affect measurement of ground reaction forces.

The purpose of this study was to carry out a material test to investigate the effect of different force plate coverings on vertical and horizontal ground reaction force and derived parameters. Four surface conditions were analysed; bare plate, vinyl, sportflex, and astroturf on a Kistler force plate. Vertical data were collected by dropping a 2 kg rigid, textured medicine ball from a low (61 cm) and a high (139 cm) height. Horizontal data were collected using a custom-built, rigid, metal pendulum device. A one-way ANOVA revealed a significant main effect of surface on peak force and rate of force development for high height, low height, and horizontal force conditions (all p<0.001), with effect sizes in the post-hoc analysis being mostly large to very large. Interestingly, sportflex yielded the highest vertical but the lowest horizontal ground reaction forces. This study showed the use of current force platform coverings had a significant effect on peak force and rate of force development measurements during a standardised testing procedure. Future research should try to obtain rate of force development values that more closely replicate aspects of human performance during standardised testing procedures. Also further investigate the effect of the different surfaces on ground reaction forces during human movement.

Robust and novel attention guided MultiResUnet model for 3D ground reaction force and moment prediction from foot kinematics

Ground reaction force and moment (GRF&M) measurements are vital for biomechanical analysis and significantly impact the clinical domain for early abnormality detection for different neurodegenerative diseases. Force platforms have become the de facto standard for measuring GRF&M signals in recent years. Although the signal quality achieved from these devices is unparalleled, they are expensive and require laboratory setup, making them unsuitable for many clinical applications. For these reasons, predicting GRF&M from cheaper and more feasible alternatives has become a topic of interest. Several works have been done on predicting GRF&M from kinematic data captured from the subject’s body with the help of motion capture cameras. The problem with these solutions is that they rely on markers placed on the whole body to capture the movements, which can be very infeasible in many practical scenarios. This paper proposes a novel deep learning-based approach to predict 3D GRF&M from only 5 markers placed on the shoe. The proposed network “Attention Guided MultiResUNet” can predict the force and moment signals accurately and reliably compared to the techniques relying on full-body markers. The proposed deep learning model is tested on two publicly available datasets containing data from 66 healthy subjects to validate the approach. The framework has achieved an average correlation coefficient of 0.96 for 3D ground reaction force prediction and 0.86 for 3D ground reaction momentum prediction in cross-dataset validation. The framework can provide a cheaper and more feasible alternative for predicting GRF&M in many practical applications.

Use of force plates to investigate human motor performance

Force plates have commonly been used to measure ground reaction forces. The acquired signals can be used to calculate several parameters for different motor tasks performed by human beings. Therefore, the goal of this tutorial is to present general information regarding the most common force plates, as well as their descriptions in terms of their structures, setup procedures, and signal acquisition, conversion, and export functions. This tutorial primarily provides information for those interested in using force plates to investigate human performances in the fields of motor behavior and biomechanics.

Controlling jumps through latches in small jumping robots

Small jumping robots can use springs to maximize jump performance, but they are typically not able to control the height of each jump owing to design constraints. This study explores the use of the jumper’s latch, the component that mediates the release of energy stored in the spring, as a tool for controlling jumps. A reduced-order model that considers the dynamics of the actuator pulling the latch and the effect of spring force on the latch is presented. This model is then validated using high speed video and ground reaction force measurements from a 4g jumper. Both the model and experimental results demonstrate that jump performance in small insect-inspired resource-constrained robots can be tuned to a range of outputs using latch mediation, despite starting with a fixed spring potential energy. For a fixed set of input voltages to the latch actuator, the results also show that a jumper with a larger latch radius has greater tunability. However, this greater tunability comes with a trade-off in maximum performance. Finally, we define a new metric, ‘Tunability Range,’ to capture the range of controllable jump behaviors that a jumper with a fixed spring compression can attain given a set of control inputs (i.e. latch actuation voltage) to choose from.

Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95-96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Relationship between latent trigger points, lower limb asymmetry and muscle fatigue in elite short-track athletes

BackgroundShort-track speed skating movement involves asymmetric overloading of the lower left side of the body. The gluteus maximus fatigue limits the physical and mental athletic capacity to perform set tasks. A possible link between the presence of latent trigger points (LTrPs) and muscle fatigue development/persistence has been posited. The aim of the study was to determine whether elite short-track speed skating can result in the impairment of the musculoskeletal system of the lower limbs.MethodsElite short-track athletes as the experimental group (EXP) = 9, 19.5 ± 1.8 years, and healthy subjects as the control group (CON) = 18, 20.8 ± 1.2 years, were tested for: (i) lower limb loading asymmetry using ground reaction force (GRF) measurements during quiet standing, (ii) gluteus maximus fatigue measured with surface electromyography (sEMG) during the Biering-Sorensen test, and (iii) LTrPs presence in the 14 examined muscles of the pelvic girdle and lower limbs.ResultsThere were between-group differences in the number of LTrPs, with the EXP group (left lower limb (LLL) n = 18, right lower limb (RLL) n = 9) showing more LTrPs compared to the CON group (LLL n = 2, RLL n = 1), (p < 0.001), and within-group differences in the EXP group only (p < 0.001). There were also significant differences in muscle fatigue for the left side (p < 0.001) both between the groups and within the EXP group (p ≤ 0.001). The vertical ground reaction force (GRF) measurement showed a loading rate of 2% (p = 0.013) in the athletes’ LLL exclusively.ConclusionsThe study confirmed an increased prevalence of LTrPs, increased muscle fatigue and left-sided limb load asymmetry in elite short-track athletes.Trial registrationThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Poznan University of Medical Sciences (Resolution No 110/22 of 10 March 2022). Trial registration: 20/07/2022, Trial Id: ACTRN12622001016729.

Wireless pressure insoles for measuring ground reaction forces and trajectories of the centre of pressure during functional activities

Wireless pressure insoles may enable the assessment of movement biomechanics in a real-world setting, and thus play an important role in the recommendation of clinical management, but they are not yet a gold standard due to the unknown accuracy and reliability with respect to different functional activities. Here, we compare novel wireless pressure insoles with force plates and examine the test–retest reliability of the insoles for measuring vertical ground reaction forces (vGRFs) and trajectories of the center of pressure (COP). In this observational study, healthy adults underwent two data collection sessions during one day. The Bland–Altman analysis was used to compare the outcomes measured with the two instruments during squats, jumps, and the sit-to-stand test. Test–retest reliability was assessed by the interclass correlation coefficient and the standard error of measurement for the outcomes during squats, jumps, walking, and stair ambulation. Trajectories of the COP in the anterior–posterior direction were comparable between the two systems during all activities. The insoles consistently measured shorter trajectories of the COP in the medial–lateral direction (except jumps) and lower vGRFs than the force plates. Test–retest reliability of the insoles was fair to high or excellent for all outcomes during all activities. In conclusion, the insoles provide reliable measures of vGRFs and trajectories of the COP during multiple functional activities in healthy adults. Although the insoles do not produce identical results to the force plate, the qualitative similarity and consistency between the two systems confirm the insoles can be used to measure these outcomes, based on the purpose and accuracy required.