Gait Phase Research Articles

Adaptive Random Forest for Gait Prediction in Lower Limb Exoskeleton

To improve the human-machine cooperativity of a wearable lower limb exoskeleton, a gait recognition method based on surface electromyography (sEMG) was proposed. sEMG of rectus femoris, vastus medialis, vastus lateralis, semitendinosus and biceps femoris were acquired. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features and the frequency domain feature includes mean power frequency. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. Classification accuracy of different feature matrices and different muscle groups were constructed and verified. The optimal multi-dimensional fusion feature matrix was determined. Introducing the Bayesian optimization algorithm, the Bayesian optimized Random Forest classification model was constructed to identify different gait phases. Comparing with Random Forest, the accuracy of the optimized Random Forest was improved by 5.89%. Applying Random Forest algorithm with Bayesian optimization to gait prediction based on sEMG, the followership and consistency of gait control in lower limb exoskeleton can be improved. This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text.

IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks.

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.9 m/s, and conditions such as asymmetric walking, stop-start, and sudden speed changes. One-subject-out cross-validation was used to assess the robustness of the estimator to the gait patterns of new individuals. The proposed model had a spatial root mean square error of 5.00±1.65%, and a temporal mean absolute error of 2.78±0.97% evaluated at the heel strike. A second cross-validation was performed to show that leaving out any of the walking conditions from the training dataset did not result in significant performance degradation. A 2-sample Kolmogorov-Smirnov test showed that there was no significant increase in spatial or temporal error when testing on the abnormal walking conditions left out of the training set. The results of the two cross-validations demonstrate that the proposed model generalizes well across new participants, various walking speeds, and gait patterns, showcasing its potential for use in investigating patient populations with pathological gaits and facilitating robot-assisted walking.

A combined musculoskeletal and finite element model of a foot to predict plantar pressure distribution

In this study, a combined subject-specific numerical and experimental investigation was conducted to explore the plantar pressure of an individual. The research utilized finite element (FE) and musculoskeletal modelling based on computed tomography (CT) images of an ankle-foot complex and three-dimensional gait measurements. Muscle forces were estimated using an individualized multi-body musculoskeletal model in five gait phases. The results of the FE model and gait measurements for the same subject revealed the highest stress concentration of 0.48 MPa in the forefoot, which aligns with previously-reported clinical observations. Additionally, the study found that the encapsulated soft tissue FE model with hyper-elastic properties exhibited higher stresses compared to the model with linear-elastic properties, with maximum ratios of 1.16 and 1.88 MPa in the contact pressure and von-Mises stress, respectively. Furthermore, the numerical simulation demonstrated that the use of an individualized insole caused a reduction of 8.3% in the maximum contact plantar pressure and 14.7% in the maximum von-Mises stress in the encapsulated soft tissue. Overall, the developed model in this investigation holds potential for facilitating further studies on foot pathologies and the improvement of rehabilitation techniques in clinical settings.

IJCM_232A: Identification of Kinematic Motifs in Crouch Gait of Children with Cerebral Palsy

Background: Crouch Gait is a fairly common pathological gait adaptation seen in children with Cerebral Palsy, characterized by inadequacy of plantar flexion, knee extension and hip extension in the stance phase of gait. From a kinetic perspective crouch gait severely impairs the ability to generate thrusting forces through the extensor muscles of stance extremity which is necessary for forward progression. Broader goal of this study was to develop and test a wearable robotic exoskeletal device for children with crouch gait, where a knee actuator provides extensor thrust. Artificial Intelligence (AI) models which control the torque output of the knee actuator, needed to be trained to activate, control, or organize the output. Objectives: Rhythmical/repetitive patterns, like the phases of normal gait, needed to be identified in crouch gait, which could be used to train AI models Methodology: A total of 30 children with cerebral palsy were screened of which 7 children aged between 7 and 14 years, who presented with classical crouch gait were recruited. Kinesiological data was collected through accelerometric and goniometric placed bilaterally on thigh, leg, and knee, as well as pressure sensors embedded on to bilateral insoles at the fore-foot and hind foot, while the children walked over a 5 meter course. All sensor data was relayed through an Arduino micro-controller unit for analog to digital conversion and stored on a laptop computer. All Accelerometer and goniometric data was plotted against time on MS Excel to identify specific patterns or motifs. Results: Key kinematic motifs could be identified in crouch gait, which bestowed semblance of rhythmicity, and cyclical pattern, which was difficult to identify under normal observation. Conclusion: Accelerometric, as well as goniometric data suggest the presence of inherent kinematic patterns in the same. These could be used for training AI models for robotic exoskeletal devices.

First tarsometatarsal joint mobility in hallux valgus during gait: A synchronized ultrasound and three-dimensional motion capture analysis

PurposeTo quantify the vertical translation between the first metatarsal and medial cuneiform during the stance phase of gait in young individuals with and without hallux valgus.DesignThis cross-sectional observational study included 34 young adults (male, n = 4; female, n = 30) who were divided into three groups according to the hallux valgus angle: control (< 20°, n = 13), mild hallux valgus (≥ 20° to < 30°, n = 12), and moderate hallux valgus (≥ 30°, n = 9). The mobility of the first tarsometatarsal joint was evaluated during the stance phase using B-mode ultrasound synchronized with a motion analysis system.ResultsThe medial cuneiform shifted more plantar during the early phase in mild hallux valgus and during the middle and terminal phases in moderate hallux valgus than in control. The severity of the hallux valgus was correlated with a trend toward plantar shift of the medial cuneiform. The first metatarsal was located more dorsal than the medial cuneiform; however, there was no significant variation. No significant differences in the peak ankle plantarflexion angle and moment were noted between the groups.ConclusionThe hypermobility of the first tarsometatarsal joint, especially plantar displacement of the medial cuneiform in the sagittal plane, was found in young individuals with hallux valgus during the stance phase of gait, and the mobility increased with the severity of hallux valgus. Our findings suggest the significance of preventing hallux valgus deformity early in life.

Trial- vs. cycle-level detrending in the analysis of cyclical biomechanical data

Biomechanical time series may contain low-frequency trends due to factors like electromechanical drift, attentional drift and fatigue. Existing detrending procedures are predominantly conducted at the trial level, removing trends that exist over finite, adjacent time windows, but this fails to consider what we term ‘cycle-level trends’: trends that occur in cyclical movements like gait and that vary across the movement cycle, for example: positive and negative drifts in early and late gait phases, respectively. The purposes of this study were to describe cycle-level detrending and to investigate the frequencies with which cycle-level trends (i) exist, and (ii) statistically affect results. Anterioposterior ground reaction forces (GRF) from the 41-subject, 8-speed, open treadmill walking dataset of Fukuchi (2018) were analyzed. Of a total of 552 analyzed trials, significant cycle-level trends were found approximately three times more frequently (21.1%) than significant trial-level trends (7.4%). In statistical comparisons of adjacent walking speeds (i.e., speed 1 vs. 2, 2 vs. 3, etc.) just 3.3% of trials exhibited cycle-level trends that changed the null hypothesis rejection decision. However 17.6% of trials exhibited cycle-level trends that qualitatively changed the stance phase regions identified as significant. Although these results are preliminary and derived from just one dataset, results suggest that cycle-level trends can contribute to analysis bias, and therefore that cycle-level trends should be considered and/or removed where possible. Software implementing the proposed cycle-level detrending is available at https://github.com/0todd0000/detrend1d.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Ankle-foot orthosis in stroke survivors: status of the issue

This paper presents an analysis of information regarding the peculiarity of ankle joint orthosis in cerebral stroke survivors. Gait research methods used to assess gait changes in patients who had suffered a cerebral stroke were briefly reviewed. The three most common patterns of gait impairment after a stroke, namely, stiff-knee gait, “dropped foot,” and gait with knee joint hyperextension, were described. A brief review of the main functional types of ankle joint orthoses, namely, static and dynamic, considering their strengths and weaknesses, was presented. Changes in temporal, spatial, and kinematic parameters of gait in cerebral stroke survivors using orthoses were reported. Changes in gait parameters when using ankle joint orthoses depending on the initial pattern of gait disturbance were assessed. The effect of orthotics on the angles of flexion in the ankle, knee, and hip joints at various gait phases as well as their effect on the speed, rhythm, and length of the step were described. Data on the effect of ankle orthosis on patient’s ability to maintain balance were also presented. The effects of orthosis on gait biomechanics and balance function in stroke survivors are debatable. Thus, our findings in this study allow us to raise the question regarding the optimal timing of the use of ankle joint orthoses during the recovery period of a stroke. Possible reasons for these ambiguous results were considered. The necessity for further research into the effectiveness of ankle–foot orthoses regarding various pathological gait patterns in stroke survivors is justified.

Weft-Knitted Strain Sensors for Motion Capture

Motion capture, especially of the knee angle, is an important component for situational triggering of functional electrical stimulation (FES). One major disadvantage of commercial FES devices is their bulky design that prevents unobtrusive wearing in everyday life and limits the patient’s free choice of appearance. This paper presents an alternative approach of sensors for motion capture in form of textile-based strain sensors. These can be integrated in a FES system in form of functional leggings, which make the FES system suitable for an unobtrusive daily use. Textile sensors, especially knitted sensors have already proven to be very promising to detect tensile strain. In particular, weft-knitted strain sensors, which can be integrated directly into the clothing during the knitting process, have the potential to detect the knee angle and therefore derive the gait phase due to the bending of the limbs without disturbing the wearer unnecessarily. Different designs of the weft-knitted strain sensor and their influence on the measurement behaviour of the sensor have been investigated. The weft-knitted strain sensor can be directly integrated in the knee area of the functional leggings to be used as a soft trigger to initiate electrical impulses for FES.

A hybrid ankle-foot orthosis with soft pneumatic actuation

This paper presents the design, development, and analysis of a powered ankle-foot orthosis for dorsiflexion assistance which aims to improve gait restoration by addressing issues related to orthosis misalignment, limited degrees of freedom, restricted range of motion, and muscular disuse. The proposed orthosis utilizes a novel hybrid design with a combination of both traditional and soft robotics for compliant and unrestrictive ankle dorsiflexion assistance for sufferers of footdrop. This article describes the complete design of the orthosis including analytical modeling and experimental testing of the soft pneumatic actuator and the development of gait phase detection and ankle angular feedback systems using wearable sensors for accurate and responsive control. Preliminary analysis was completed which validates the orthosis as a lightweight, unrestrictive, and compliant device that is capable of dorsiflexing the ankle of a person up to 100 kg, and at walking speeds appropriate for safe and effective community ambulation of up to 1.04 m/s. The novel design of the device demonstrates the potential for improved rehabilitative outcomes for patients with footdrop, due to the ability to adjust the assistive force of the device, throughout the progression of rehabilitation, which encourages muscular participation of the user and therefore reduces issues caused by muscular disuse.

Influence of Talar and Calcaneal Morphology on Subtalar Kinematics During Walking.

Cadaver biomechanical testing suggests that the morphology of articulating bones contributes to the stability of the joints and determines their kinematics; however, there are no studies examining the correlation between bone morphology and kinematics of the subtalar joint. The purpose of this study was to investigate the influence of talar and calcaneal morphology on subtalar kinematics during walking in healthy individuals. Forty ankles (20 healthy subjects, 10 women/10 men) were included. Participants walked at a self-selected pace while synchronized biplane radiographs of the hindfoot were acquired at 100 images per second during stance. Motion of the talus and calcaneus was tracked using a validated volumetric model-based tracking process, and subtalar kinematics were calculated. Talar and calcaneal morphology were evaluated using statistical shape modeling. Pearson correlation coefficients were used to assess the relationship between subtalar kinematics and the morphology features of the talus and calcaneus. This study found that a shallower posterior facet of the talus was correlated with the subtalar joint being in more dorsiflexion, more inversion, and more internal rotation, and higher curvature in the posterior facet was correlated with more inversion and eversion range of motion during stance. In the calcaneus, a gentler slope of the middle facet was correlated with greater subtalar inversion. The morphology of the posterior facet of the talus was found to a primary factor driving multiplanar subtalar joint kinematics during the stance phase of gait. This new knowledge relating form and function in the hindfoot may assist in identifying individuals susceptible to subtalar instability and in improving implant design to achieve desired kinematics after surgery.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units.

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

Sensitivity analysis of paediatric knee kinematics to the graft surgical parameters during anterior cruciate ligament reconstruction: A sequentially linked neuromusculoskeletal-finite element analysis

Background and objectiveIncidence of paediatric anterior cruciate ligament (ACL) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence guiding these decisions. This study presents a systemmatic computational approach to study effects of surgical parameter variation on kinematics of paediatric knees. MethodsThis study used sequentially-linked neuromusculoskeletal (NMSK) finite element (FE) models of three paediatric knees to estimate the: (i) sensitivity of post-operative knee kinematics to four surgical parameters (type, size, insertion, and pre-tension) through multi-input multi-output sensitivity analysis; (ii) influence of motion and loading conditions throughout stance phase of walking gait on sensitivity indices; and (iii) influence of subject-specific anatomy (i.e., knee size) on sensitivivty indices. A previously validated FE model of the intact knee for each subject served as a reference against which ACLR knee kinematics were compared. ResultsSensitivity analyses revealed significant influences of surgical parameters on ACLR knee kinematics, albeit without discernible trend favouring any one parameter. Graft size and pre-tension were primary drivers of variation in knee translations and rotations, however, their effects fluctuated across stance indicating motion and loading conditions affect system sensitivity to surgical parameters. Importantly, the sensitivity of knee kinematics to surgical parameter varied across subjects, indicating geometry (i.e., knee size) influenced system sensitivity. Notably, alterations in graft parameters yielded substantial effects on kinematics (normalized root-mean-square-error > 10 %) compared to intact knee models, indicating surgical parameters vary post-operative knee kinematics. ConclusionsOverall, this initial study highlights the importance of surgical parameter selection on post-operative kinematics in the paediatric ACLR knee, and provides evidence of the need for personalized surgical planning to ultimately enhance patient outcomes.

IMUs Can Estimate Hip and Knee Range of Motion during Walking Tasks but Are Not Sensitive to Changes in Load or Grade.

The ability to estimate lower-extremity mechanics in real-world scenarios may untether biomechanics research from a laboratory environment. This is particularly important for military populations where outdoor ruck marches over variable terrain and the addition of external load are cited as leading causes of musculoskeletal injury As such, this study aimed to examine (1) the validity of a minimal IMU sensor system for quantifying lower-extremity kinematics during treadmill walking and running compared with optical motion capture (OMC) and (2) the sensitivity of this IMU system to kinematic changes induced by load, grade, or a combination of the two. The IMU system was able to estimate hip and knee range of motion (ROM) with moderate accuracy during walking but not running. However, SPM analyses revealed IMU and OMC kinematic waveforms were significantly different at most gait phases. The IMU system was capable of detecting kinematic differences in knee kinematic waveforms that occur with added load but was not sensitive to changes in grade that influence lower-extremity kinematics when measured with OMC. While IMUs may be able to identify hip and knee ROM during gait, they are not suitable for replicating lab-level kinematic waveforms.

Anterior Talofibular Ligament Repair in Combination With Anterior Tibiofibular Ligament Distal Fascicle Transfer for The Treatment of Chronic Lateral Ankle Instability: A Finite Element Analysis

In recent years, anterior tibiofibular ligament-distal fascicle transfers for anterior talofibular ligament augmentation repair have proposed. However, a comprehensive biomechanical study on the anterior tibiofibular ligament-distal fascicle transfer is still lacking. We are established four distinct groups, namely the normal, the anterior talofibular ligament rupture, the anterior talofibular ligament repair, and the anterior talofibular ligament repair + anterior tibiofibular ligament-distal fascicle transfer. We assessed the anterior drawer test and varus stress test of the ankle in each group. Moreover, we employed the model to simulate and compute the total displacement and von-Mises stress of the talus cartilage at varying gait phases, including foot strike, tibia vertical, and toe-off phases. The results of the anterior drawer test and varus stress test revealed that the anterior talofibular ligament repair + anterior tibiofibular ligament-distal fascicle transfer group exhibited greater closeness to the normal group. Regarding von-Mises stress in cartilage, the three gait instants had higher values in the anterior talofibular ligament repair + anterior tibiofibular ligament-distal fascicle transfer group than the other groups. Nevertheless, regarding total displacement, the toe-off phases exhibited higher values in the anterior talofibular ligament repair + anterior tibiofibular ligament-distal fascicle transfer group than the other groups. Using ATiFL-DF transfer to augment ATFL repair is a potential feasible procedure. However, this procedure could potentially compromise the anterior tibiofibular ligament's contribution to the dynamic stability of the ankle. Therefore, we recommend conducting further in-depth research to ensure the suitability and success of this technique in a clinical environment.

Gait analysis of male professional boxers

Introduction: Gait plays a crucial role in both daily life and sports performance. This study analyzes professional boxers’ gait, as it is essential to understand its influence on performance and injury risk. However, there is a need to comprehend the effects of boxing training on gait and the occurrence of asymmetries between limbs. Research Question: Does the gait of professional boxers exhibit significant differences between limbs, and what are the potential implications of this asymmetry? Materials and Methods: The study involved 36 professional boxers, and the Gaitway 3D Pressure treadmill was used to collect data. The analysis was performed using statistical tests with a pre-determined level of significance. Results: Significant differences were found between limbs in gait phase parameters and ground reaction forces. A longer swing phase of the right leg was observed, potentially affecting the reduction of single-limb support and right-leg propulsion. Additionally, higher ground reaction forces were noted on the left side. Conclusions: This analysis of gait in professional boxers provides valuable insights into the asymmetry between limbs, which may impact performance and injury risk. It also shows the correct characteristics of selected gait parameters. These findings are relevant for tailoring training programs, reducing injuries, and enhancing overall physical conditioning of athletes. Further-more, they can be applied in both sports and daily life for injury prevention and rehabilitation, where maintaining balance and stability are crucial in avoiding injuries and improving the quality of life.

A Method for Diabetic Wound-Specific Insole Design, Manufacturing, and Biomechanical Validation for Better Recovery.

Muscle disorders may cause a change in plantar pressures by the misalignment on the foot during gait phases. Therefore, corns or calluses develop at the plantar regions, and diabetic foot ulcers follow for severe cases, although it can be prevented and even treated by podiatric approaches with patient-specific therapeutic insoles and footwear. Although the importance of a threshold value of 200 kPa in peak plantar pressure reduction has been highlighted as a standard to prevent reulceration in the diabetic foot, it may not be possible to ensure this pressure reduction for each patient. In this study, three types of ethylene-vinyl acetate have been used to optimize the off-loading performance for predetermined early-stage diabetic foot ulcer scenarios by means of baropodometric plantar pressure analyses and finite element method for each gait phase. The total cost of the manufacturing for this study was reduced to $10.26 and it was performed in 24.6 minutes. In addition, the off-loaded pressure was increased by 2.3 times and the volume of the off-loading geometry was increased 8.12 times based on the foam polymer used. Consequently, improved off-loading was obtained and a standard was proposed for the first time to calculate the off-loading performance before manufacturing of the therapeutic insole model to ensure a better recovery period.

Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-Based IMU Acceleration? A Novel Method of Conducting Biomechanical Analysis in the Field Using Artificial Neural Networks

This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed treadmill running trials at several speeds. Participants wore a GPS device containing a triaxial accelerometer, and running kinematics were captured by an 18-camera motion capture system for each trial. Multiple multilayer perceptron neural network models were constructed to estimate participants’ 3D running kinematics. The models consisted of the following input variables: 3D peak accelerometer acceleration during foot stance (g), stance time (s), running speed (km/h), participant height (cm), leg length (cm), and mass (kg). Pearson’s correlation coefficient (r), root mean squared error (RMSE), and relative root mean squared error (rRMSE) showed that ANN models provide accurate estimations of joint/segment angles (mean rRMSE = 13.0 ± 4.3%) and peak segment velocities (mean rRMSE = 22.1 ± 14.7%) at key gait phases across foot stance. The highest accuracies were achieved for flexion/extension angles of the thorax, pelvis, and hip, and peak thigh flexion/extension and vertical velocities (rRMSE &lt; 10%). The current findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics using a single IMU.

Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject's Gait Progression Using Wearable Inertial Sensor.

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model's recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%.

Propulsive forces and muscle activation during gait: comparisons between premenopausal and postmenopausal midlife women.

The aim of this study was to investigate whether there is a reduction in propulsive force during gait in postmenopausal women compared with premenopausal women. Forty-four women (21 premenopausal and 23 postmenopausal women) aged 40 to 55 years were selected. The ability to reach peak propulsive forces was assessed during the step execution test. The test was performed at the usual speed on 2 nonconsecutive days, with two attempts per day, using a force platform. Four temporal parameters were defined and calculated: initiation phase, preparation phase, swing phase, and total time. Peak force (anteroposterior and vertical) and time to reach peak force were obtained in both preparation and swing phases. The rate of force development was defined as peak force divided by time to reach peak force. The postmenopausal women group presented a longer time in the preparation phase (540.6 ± 77 ms vs 482.5 ± 93 ms, P = 0.024) and consequently a longer total time in the step execution test (1,191 ± 106.4 ms vs 1,129 ± 114.3 ms, P = 0.045). There were differences between the groups for the rate of force development in the anteroposterior (postmenopausal women, 142.5 ± 38.1 N/s vs premenopausal women, 174.7 ± 70.5 N/s; P = 0.022) and vertical directions in the preparation phase (postmenopausal women, 102.7 ± 62.3 N/s vs premenopausal women, 145.3 ± 71 N/s; P = 0.012). No significant differences ( P > 0.05) were found in force, time to peak force, and rate of force development during the swing phase. In addition, there were no observed differences in surface electromyography of the medial and lateral gastrocnemius muscles during the preparation phase and swing phase of the step execution test between the two groups. Postmenopausal women exhibited lower ability to generate propulsive force rapidly (rates of force development) in both the anteroposterior and vertical directions during the preparation phase of gait compared with premenopausal women. This indicates that postmenopausal women experience a reduction in propulsive force during gait.