Search

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method.

Total ankle arthroplasty (TAA) is a motion-preserving treatment for end-stage ankle arthritis. An effective tool for analyzing these implants’ mechanical performance and longevity in silico is finite element analysis (FEA). An FEA in ABAQUS was used to statically analyze the mechanical behavior of the ultra-high-molecular-weight polyethylene (UHMWPE) bearing component at varying dorsiflexion/plantarflexion ankle angles and axial loading conditions during the stance phase of the gait cycle for a single cycle. The von Mises stress and contact pressure were examined on the articulating surface of the bearing component in two newly installed fixed-bearing TAA implants (Wright Medical INBONE II and Exactech Vantage). Six different FEA models of variable ankle compressive load levels and ankle angle positions, for the varying subphases of the stance phase of the gait cycle, were created. The components in these models were constrained to be conducive to the bone–implant interface, where implant loosening occurs. Our results showed that the von Mises stress and contact pressure distributions increased as the compressive load increased. The highest stress was noted at dorsiflexion angles > 15°, in areas where the UHMWPE liner was thinnest, at the edges of the talar and UHMWPE components, and during the terminal stance phase of the gait cycle. This static structural analysis highlighted these failure regions are susceptible to yielding and wear and indicated stress magnitudes that are in agreement (within 25%) with those in previous static structural TAA FEAs. The mechanical wear of the UHMWPE bearing component in TAA can lead to aseptic loosening and peri-implant cyst formation over time, requiring surgical revision. This study provides ankle replacement manufacturers and orthopedic surgeons with a better understanding of the stress response and contact pressure sustained by TAA implants, which is critical to optimizing implant longevity and improving patient care.

Abstract Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.

Total ankle arthroplasty (TAA) is a motion-preserving treatment for end-stage ankle arthritis. An effective tool for analyzing these implants' mechanical performance and longevity in silico is finite element analysis (FEA). An FEA in ABAQUS was used to statically analyze the mechanical behavior of the ultra-high-molecular-weight polyethylene (UHMWPE) bearing component at varying dorsiflexion/plantarflexion ankle angles and axial loading conditions during the stance phase of the gait cycle for a single cycle. The von Mises stress and contact pressure were examined on the articulating surface of the bearing component in two newly installed fixed-bearing TAA implants (Wright Medical INBONE II and Exactech Vantage). Six different FEA models of variable ankle compressive load levels and ankle angle positions, for the varying subphases of the stance phase of the gait cycle, were created. The components in these models were constrained to be conducive to the bone–implant interface, where implant loosening occurs. Our results showed that the von Mises stress and contact pressure distributions increased as the compressive load increased. The highest stress was noted at dorsiflexion angles > 15°, in areas where the UHMWPE liner was thinnest, at the edges of the talar and UHMWPE components, and during the terminal stance phase of the gait cycle. This static structural analysis highlighted these failure regions are susceptible to yielding and wear and indicated stress magnitudes that are in agreement (within 25%) with those in previous static structural TAA FEAs. The mechanical wear of the UHMWPE bearing component in TAA can lead to aseptic loosening and peri-implant cyst formation over time, requiring surgical revision. This study provides ankle replacement manufacturers and orthopedic surgeons with a better understanding of the stress response and contact pressure sustained by TAA implants, which is critical to optimizing implant longevity and improving patient care. [ABSTRACT FROM AUTHOR]

Among older people, walking difficulty results from actual and perceived declines in physical capacities and environmental requirements for walking. We investigated whether the physiological complexity of the gait cycle covaries with experience of walking difficulty. Walking difficulty, gait speed, and gait cycle complexity were evaluated among 702 community-dwelling older people aged 75, 80, and 85 years who took part in the six-minute walking test in the research laboratory. Walking difficulty for 500 m was self-reported. Complexity was quantified as trunk acceleration multiscale entropy during the gait cycle. Complexity was then compared between those with no reported walking difficulty, walking with modifications but no difficulty, and those reporting walking difficulty. Higher entropy differentiated those reporting no difficulty walking from those reporting walking difficulties, while those reporting having modified their walking, but no difficulty formed an intermediate group that could not be clearly distinguished from the other categories. The higher complexity of the gait cycle is associated with slower gait speed and the presence of self-reported walking difficulty. Among older people, gait cycle complexity which primarily reflects the biomechanical dimensions of gait quality, could be a clinically meaningful measure reflecting specific features of the progression of walking decline. This encourages further investigation of the sensitivity of gait cycle complexity to detect early signs of gait deterioration and to support targeted interventions among older people.

For time-continuous analysis of gait, the problem of variations in cycle durations is resolved by normalizing to the gait cycle, but results depend on the definition of the cycle start. Gait cycle normalization ignores variations in gait phase durations, which results in averaging and comparing data across different phases. We propose gait phase normalization as part of a comprehensive method for independently analyzing magnitude and timing differences. First, gait phases are identified and differences in absolute and/or relative timing of phase durations or any point of interest between conditions or groups are analyzed using standard statistics. Next, time-continuous gait data is normalized to gait phases, and statistical parametric mapping (SPM) is used to assess magnitude differences in gait data. This approach is demonstrated on data recorded from ten young healthy adults walking on a treadmill at five different speeds. Sagittal knee angle was normalized to gait cycle or gait phase using five different gait cycle start events. Walking at different speeds resulted in significant changes in gait phase durations, highlighting a problem ignored by gait cycle normalization. SPM results for knee angle normalized to gait cycle varied from normalization to gait phases. Gait phase normalized SPM results were robust to the definition of the cycle start, in contrast to gait cycle normalized data. The approach of analyzing phase durations and normalizing data to gait phases overcomes previous limitations and enables a comprehensive analysis of magnitude and timing differences in time-continuous gait data and could be readily adapted to other tasks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters.

Context: This work presents the development of a dynamic model for human lower limb motion in the sagittal plane during the gait cycle. The primary objective of this model is to serve as a powerful tool for the design of rehabilitation and assistive devices, such as exoskeletons, prostheses, and orthoses. It achieves this by facilitating the estimation of joint torques, the detailed analysis of kinematic variables, optimal actuator selection, and the exploration of advanced control techniques. Method: The dynamic model consists of two primary components: (1) the plant model and (2) a closed-loop controller. The plant model represents the forward dynamics of human gait and is based on a multi-mass pendulum composed of three segments of the lower limb (thigh, lower leg, and foot) and three joints (hip, knee, and ankle). It is analyzed using the Euler-Lagrange formulation and the nonlinear second-order differential equations are implemented in MATLAB’s Simulink. To reproduce reference human gait trajectories and simulate the functioning of the neuromusculoskeletal system and the central nervous system, a closed-loop PID controller is incorporated into the plant model. It is noteworthy that the scope of this dynamic model is specifically confined to the sagittal plane. Results: The dynamic model is evaluated in terms of angular displacement tracking using the relative maximum error (RME) and the root mean square error (RMSE) for reference trajectories of healthy adult male human gait as reported in the literature. The model demonstrates tracking with errors below 2.2 [°] in magnitude and 3,5% for all three considered segments (thigh, lower leg, and foot). Conclusions: The quantitative results show that the dynamic model developed in this work is reliable and allows for a precise reproduction of human gait trajectories.

Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized. [ABSTRACT FROM AUTHOR]

This article introduces a data‐driven locomotion style controller for full‐body human characters using gait cycle features. Based on gait analysis, we define a set of gait features that can represent various locomotion styles as spatio‐temporal patterns within a single gait cycle. We compute the gait features for every single gait cycle in motion capture data and use them to search for the desired motion. Our real‐time style controller provides users with visual feedback for the changing inputs, exploiting the Motion Matching algorithm. We also provide a graphical controller interface that visualizes our style representation to enable intuitive control for users. We show that the proposed method is capable of retrieving appropriate locomotions for various gait cycle features, from simple walking motions to single‐foot motions such as hopping and dragging. To validate the effectiveness of our method, we conducted a user study that compares the usability and performance of our system with those of an existing footstep animation tool. The results show that our method is preferred over the baseline method for intuitive control and fast visual feedback. [ABSTRACT FROM AUTHOR]

Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters. [ABSTRACT FROM AUTHOR]

Objective: Electroencephalography (EEG) with high time-resolution allows for recording dynamic cortical activity during walking and provides new insight into the underlying pathophysiology of gait impairments in PD. However, traditional gait-phase-specific EEG analysis only measures the brain activities in the isolated gait phase, but neglects the between-gait-phase interactions as well as the whole-gait-cycle characteristics, and therefore is unable to effectively reflect the abnormal cortical gait control., Methods: In this study, we introduced three whole-gait-cycle measures of intra-stride EEG activity (i.e., mean desynchronization, amplitude of fluctuations, and coupling to the gait phase), and investigated their abnormalities in PD and relationships with gait impairments, which were further compared with the traditional gait-phase-specific measures., Results: Compared with healthy controls, PD patients showed overwhelming stronger desynchronizations (ERD) across the whole gait cycle in θ, α and low-β bands, implying a cortical compensatory strategy in response to the low efficiency of the motor network. Patients also exhibited weaker intra-stride ERD fluctuations in the central area in α and low-β bands, with reduced amplitude and less coupling to the gait phase, which were correlated with gait impairments in walking speed, gait rhythm and walking stability. However, gait-phase-specific EEG measures did not show any significant correlation with gait impairments in PD., Conclusion: Our results demonstrated the efficiency of whole-gait-cycle EEG measures in characterizing the abnormal cortical gait control, and for the first time, associated gait impairments with weak intra-stride electrocortical fluctuations., Significance: The findings may shed light on the development of cortical-targeted interventions for PD.

The running-in process is considered an essential aspect of the comprehensive wear process. The phenomenon of running-in occurs during the initial stages of wear in the prosthetic hip joint. Within the field of tribology, the running-in phenomenon of the hip joint pertains to the mechanism by which the contact surfaces of the artificial hip joint components are adjusted and a suitable lubricating film is formed. During the process of hip joint running-in, there is an interaction between the metal surface of the ball and the joint cup, which results in adjustments being made until a steady state is achieved. The achievement of desirable wear existence and reliable performance of artificial hip joint components are reliant upon the tribological running-in of the hip joint. Despite the establishment of current modeling approaches, there remains a significant lack of understanding concerning running-in wear, particularly the metal-on-polyethylene (MoP) articulations in dual-mobility cups (DMC). An essential aspect to consider is the running-in phase of the dual mobility component. The present study employed finite element analysis to investigate the running-in behavior of dual mobility cups, wherein femoral head components were matched with polyethylene liners of varying thicknesses. The analysis of the running-in phase was conducted during the normal gait cycle. The results of this investigation may be utilized to design a dual-mobility prosthetic hip joint that exhibits minimal running-in wear., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method. [ABSTRACT FROM AUTHOR]

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participants level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the bodys center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

The gait cycle of 25-degree of freedom (DOF) humanoid robot, namely NAO robot, consists of single support phase (SSP) and double support phase (DSP). Both dynamic and stability analyses are carried out for this robot to determine its power consumption and dynamic stability margin, respectively. Constrained single-objective optimization problems are formulated for the SSP and DSP separately and solved using particle swarm optimization (PSO) and genetic algorithms (GA). A performance index, other than the fitness function, consisting of constraint values and maximum swing height, is also considered to compare PSO and GA-obtained optimal solutions. PSO is able to find the trajectories that offer higher swing height for nearly similar power consumption during SSP. A performance assessment of each algorithm based on the best fitness values in each generation across several runs is also carried out. These values are compared using the Wilcoxon rank-sum test, and PSO is found to be statistically better than GA. The optimal solutions from the simulations are tested using the Webots simulator to validate their efficacy on stability. Moreover, an investigation of the influence of gait parameters on power consumption during SSP and DSP reveals that the humanoid robot with a higher hip height, lower swing height, and slow pace consumes less power. The methodology developed in this is generic and can be easily extended to other robots. [ABSTRACT FROM AUTHOR]

The study of the phenomena occurring in the plantar region is remarkably intriguing, especially when performing a normal gait cycle where the foot is under loading conditions. The effects presented in the foot while walking provide relevant indicators regarding clinical means for enhancing regular performance or rehabilitation therapies. Nevertheless, more than traditional methods are needed to biomechanically evaluate foot structural conditions, leading to an incomplete database for determining the patient’s needs so that advanced methodologies provide detailed medical assessment. Therefore, it is necessary to employ technological engineering tools to optimize biomechanical plantar pressure evaluations to reach suitable personalized treatments. This research initially evaluated numerically the pressure points in the foot sole region in each one of the five stance phases in a normal gait cycle. Medical imaging techniques were utilized to construct an anatomically accurate biomodel of the soft tissues of the right foot. The Finite Element Method was employed to predict peak plantar pressure in barefoot conditions for all stance phases; results from this case study presented a close alignment with gait experimental testing implemented to analyze the feasibility and validation of all mechanical considerations for the numerical analyses. Hence, having a solid foundation in the biomechanical behavior from the first case study close estimates, a 3D-printable patient-specific insole was designed and numerically analyzed to observe the mechanical response in the plantar critical zones utilizing a personalized orthotic device. Results from the second case study notably demonstrated a crucial decrement in excessive pressure values. Employing morphological customization orthopedics modeling combined with 3D-printable materials is revolutionizing assistive device design and fabrication techniques. The fundamental contribution of this research relies on deepening the knowledge of foot biomechanics from an interdisciplinary approach by numerically analyzing pressure distribution in critical regions for all five stances phases; thus, based on the methods employed, the results obtained contribute to the advances of patient-specific foot orthopedics.