A force plate study of avian gait

OBJECTIVE: To test the force plate as a gait analysis system for broilers and to determine how the ground reaction force (GRF) patterns change in these birds with growth and administration of analgesia. MATERIALS AND METHODS: Thirty-three male Ross 308 chicks were raised on either an ad libitum or restricted-feeding regime, and subsequently treated with carprofen or a placebo. Vertical, craniocaudal and mediolateral GRFs were measured as the birds walked across a standard force plate. RESULTS: The data were easy to collect, and peak vertical forces of an equivalent percentage of bodyweight as seen in human walking were identified. Mediolateral forces were 2-3 times greater than those demonstrated in other species. GRF patterns showed significant changes during growth, but analgesia did not have a significant effect on the speed of walking, or GRF patterns. CONCLUSIONS AND CLINICAL RELEVANCE: The force plate is a suitable research tool for recording GRFs from avian bipeds. The large mediolateral forces identify a particularly inefficient aspect of avian gait; however, the role of pain remains to be determined.     

Prediction of ground reaction forces during gait based on kinematics and a neural network model

Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial–lateral axis, 0.985 in the anterior–posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.     

A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation

In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.     

A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation

In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.     

Resonance-based oscillations could describe human gait mechanics under various loading conditions

The oscillatory behavior of the center of mass (CoM) and the corresponding ground reaction force (GRF) of human gait for various gait speeds can be accurately described in terms of resonance using a spring–mass bipedal model. Resonance is a mechanical phenomenon that reflects the maximum responsiveness and energetic efficiency of a system. To use resonance to describe human gait, we need to investigate whether resonant mechanics is a common property under multiple walking conditions. Body mass and leg stiffness are determinants of resonance; thus, in this study, we investigated the following questions: (1) whether the estimated leg stiffness increased with inertia, (2) whether a resonance-based CoM oscillation could be sustained during a change in the stiffness, and (3) whether these relationships were consistently observed for different walking speeds. Seven healthy young subjects participated in over-ground walking trials at three different gait speeds with and without a 25-kg backpack. We measured the GRFs and the joint kinematics using three force platforms and a motion capture system. The leg stiffness was incorporated using a stiffness parameter in a compliant bipedal model that best fitted the empirical GRF data. The results showed that the leg stiffness increased with the load such that the resonance-based oscillatory behavior of the CoM was maintained for a given gait speed. The results imply that the resonance-based oscillation of the CoM is a consistent gait property and that resonant mechanics may be useful for modeling human gait.     

Use of a backpack alters gait initiation of high school students

We assessed how backpack carriage influences the gait initiation (GI) process in high school students, who extensively use backpacks. GI involves different dynamics from gait itself, while the excessive use of backpacks can result in adverse effects. 117 high school students were evaluated in three experimental conditions: no backpack (NB), bilateral backpack (BB), and unilateral backpack (UB). Two force plates were used to acquire ground reaction forces (GRFs) and moments for each foot separately. Center of pressure (COP) scalar variables were extracted, and statistical parametric mapping analysis was performed over the entire COP/GRFs time series. GI anticipatory postural adjustments (APAs) were reduced and were faster in backpack conditions; medial–lateral COP excursion was smaller in this phase. The uneven distribution of the extra load in the UB condition led to a larger medial–lateral COP shift in the support-foot unloading phase, with a corresponding vertical GRF change that suggests a more pronounced unloading swing foot/loading support foot mechanism. The anterior–posterior GRFs were altered, but the COP was not. A possible explanation for these results may be the forward trunk lean and the center of mass proximity of the base of support boundary, which induced smaller and faster APA, increased swing foot/support foot weight transfer, and increased load transfer to the first step.     

Kinematic adaptations to a variable stiffness shoe: mechanisms for reducing joint loading

A recently described variable-stiffness shoe has been shown to reduce the adduction moment and pain in patients with medial-compartment knee osteoarthritis. The mechanism associated with how this device modifies overall gait patterns to reduce the adduction moment is not well understood. Yet this information is important for applying load modifying intervention for the treatment of knee osteoarthritis. A principal component analysis (PCA) was used to test the hypothesis that there are differences in the frontal plane kinematics that are correlated with differences in the ground reaction forces (GRFs) and center of pressure (COP) for a variable-stiffness compared to a constant-stiffness control shoe. Eleven healthy adults were tested in a constant-stiffness control shoe and a variable-stiffness shoe while walking at self-selected speeds. The PCA was performed on trial vectors consisting of all kinematic, GRF and COP data. The projection of trial vectors onto the linear combination of four PCs showed there were significant differences between shoes. The interpretation of the PCs indicated an increase in the ankle eversion, knee abduction and adduction, decreases in the hip adduction and pelvic obliquity angles and reduced excursion of both the COP and peak medial-lateral GRFs for the variable-stiffness compared to the control shoe. The variable-stiffness shoe produced a unique dynamic change in the frontal plane motion of the ankle, hip and pelvis that contributed to changes in the GRF and COP and thus reduced the adduction moment at a critical instant during gait suggesting a different mechanism that was seen with fixed interventions (e.g. wedges).     

Effects of velocity and weight support on ground reaction forces and metabolic power during running

The biomechanical and metabolic demands of human running are distinctly affected by velocity and body weight. As runners increase velocity, ground reaction forces (GRF) increase, which may increase the risk of an overuse injury, and more metabolic power is required to produce greater rates of muscular force generation. Running with weight support attenuates GRFs, but demands less metabolic power than normal weight running. We used a recently developed device (G-trainer) that uses positive air pressure around the lower body to support body weight during treadmill running. Our scientific goal was to quantify the separate and combined effects of running velocity and weight support on GRFs and metabolic power. After obtaining this basic data set, we identified velocity and weight support combinations that resulted in different peak GRFs, yet demanded the same metabolic power. Ideal combinations of velocity and weight could potentially reduce biomechanical risks by attenuating peak GRFs while maintaining aerobic and neuromuscular benefits. Indeed, we found many combinations that decreased peak vertical GRFs yet demanded the same metabolic power as running slower at normal weight. This approach of manipulating velocity and weight during running may prove effective as a training and/or rehabilitation strategy.     

Estimation of unmeasured ground reaction force data based on the oscillatory characteristics of the center of mass during human walking

To enhance the wearability of portable motion-monitoring devices, the size and number of sensors are minimized, but at the expense of quality and quantity of data collected. For example, owing to the size and weight of low-frequency force transducers, most currently available wearable gait measurement systems provide only limited, if any, elements of ground reaction force (GRF) data. To obtain the most GRF information possible with a minimal use of sensors, we propose a GRF estimation method based on biomechanical knowledge of human walking. This includes the dynamics of the center of mass (CoM) during steady human gait resembling the oscillatory behaviors of a mass-spring system. Available measurement data were incorporated into a spring-loaded inverted pendulum with translating pivot. The spring stiffness and simulation parameters were tuned to match, as accurately as possible, the available data and oscillatory characteristics of walking. Our results showed that the model simulation estimated reasonably well the unmeasured GRF. Using the vertical GRF and CoP profile for gait speeds ranging from 0.93 to 1.89 m/s, the anterior-posterior (A-P) GRF was estimated and resulted in an average correlation coefficient of R = 0.982 ± 0.009. Even when the ground contact timing and gait speed information were alone available, our method estimated GRFs resulting in R = 0.969 ± 0.022 for the A-P and R = 0.891 ± 0.101 for the vertical GRFs. This research demonstrates that the biomechanical knowledge of human walking, such as inherited oscillatory characteristics of the CoM, can be used to gain unmeasured information regarding human gait dynamics.     

Muscular contributions to hip and knee extension during the single limb stance phase of normal gait: a framework for investigating the causes of crouch gait

Crouch gait, a troublesome movement abnormality among persons with cerebral palsy, is characterized by excessive flexion of the hips and knees during stance. Treatment of crouch gait is challenging, at present, because the factors that contribute to hip and knee extension during normal gait are not well understood, and because the potential of individual muscles to produce flexion or extension of the joints during stance is unknown. This study analyzed a three-dimensional, muscle-actuated dynamic simulation of walking to quantify the angular accelerations of the hip and knee induced by muscles during normal gait, and to rank the potential of the muscles to alter motions of these joints. Examination of the muscle actions during single limb stance showed that the gluteus maximus, vasti, and soleus make substantial contributions to hip and knee extension during normal gait. Per unit force, the gluteus maximus had greater potential than the vasti to accelerate the knee toward extension. These data suggest that weak hip extensors, knee extensors, or ankle plantar flexors may contribute to crouch gait, and strengthening these muscles--particularly gluteus maximus--may improve hip and knee extension. Abnormal forces generated by the iliopsoas or adductors may also contribute to crouch gait, as our analysis showed that these muscles have the potential to accelerate the hip and knee toward flexion. This work emphasizes the need to consider how muscular forces contribute to multijoint movements when attempting to identify the causes of abnormal gait.     

Leg stiffness increases with speed to modulate gait frequency and propulsion energy

Bipedal walking models with compliant legs have been employed to represent the ground reaction forces (GRFs) observed in human subjects. Quantification of the leg stiffness at varying gait speeds, therefore, would improve our understanding of the contributions of spring-like leg behavior to gait dynamics. In this study, we tuned a model of bipedal walking with damped compliant legs to match human GRFs at different gait speeds. Eight subjects walked at four different gait speeds, ranging from their self-selected speed to their maximum speed, in a random order. To examine the correlation between leg stiffness and the oscillatory behavior of the center of mass (CoM) during the single support phase, the damped natural frequency of the single compliant leg was compared with the duration of the single support phase. We observed that leg stiffness increased with speed and that the damping ratio was low and increased slightly with speed. The duration of the single support phase correlated well with the oscillation period of the damped complaint walking model, suggesting that CoM oscillations during single support may take advantage of resonance characteristics of the spring-like leg. The theoretical leg stiffness that maximizes the elastic energy stored in the compliant leg at the end of the single support phase is approximated by the empirical leg stiffness used to match model GRFs to human GRFs. This result implies that the CoM momentum change during the double support phase requires maximum forward propulsion and that an increase in leg stiffness with speed would beneficially increase the propulsion energy. Our results suggest that humans emulate, and may benefit from, spring-like leg mechanics.     

Influence of altered gait patterns on the hip joint contact forces

Children who exhibit gait deviations often present a range of bone deformities, particularly at the proximal femur. Altered gait may affect bone growth and lead to deformities by exerting abnormal stresses on the developing bones. The objective of this study was to calculate variations in the hip joint contact forces with different gait patterns. Muscle and hip joint contact forces of four children with different walking characteristics were calculated using an inverse dynamic analysis and a static optimisation algorithm. Kinematic and kinetic analyses were based on a generic musculoskeletal model scaled down to accommodate the dimensions of each child. Results showed that for all the children with altered gaits both the orientation and magnitude of the hip joint contact force deviated from normal. The child with the most severe gait deviations had hip joint contact forces 30% greater than normal, most likely due to the increase in muscle forces required to sustain his crouched stance. Determining how altered gait affects joint loading may help in planning treatment strategies to preserve correct loading on the bone from a young age.     

Comparison among probabilistic neural network,support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait

Deep brain stimulation of the subthalamic nucleus (DBS-STN) is an approved treatment for advanced Parkinson disease (PD) patients; however, there is a need to further evaluate its effect on gait. This study compares logistic regression (LR), probabilistic neural network (PNN) and support vector machine (SVM) classifiers for discriminating between normal and PD subjects in assessing the effects of DBS-STN on ground reaction force (GRF) with and without medication. Gait analysis of 45 subjects (30 normal and 15 PD subjects who underwent bilateral DBS-STN) was performed. PD subjects were assessed under four test conditions: without treatment (mof-sof), with stimulation alone (mof-son), with medication alone (mon-sof), and with medication and stimulation (mon-son). Principal component (PC) analysis was applied to the three components of GRF separately, where six PC scores from vertical, one from anterior–posterior and one from medial–lateral were chosen by the broken stick test. Stepwise LR analysis employed the first two and fifth vertical PC scores as input variables. Using the bootstrap approach to compare model performances for classifying GRF patterns from normal and untreated PD subjects, the first three and the fifth vertical PCs were attained as SVM input variables, while the same ones plus the first anterior–posterior were selected as PNN input variables. PNN performed better than LR and SVM according to area under the receiver operating characteristic curve and the negative likelihood ratio. When evaluating treatment effects, the classifiers indicated that DBS-STN alone was more effective than medication alone, but the greatest improvements occurred with both treatments together.     

PCA-based SVM for automatic recognition of gait patterns

In this technical note, we investigate a combination PCA with SVM to classify gait pattern based on kinetic data. The gait data of 30 young and 30 elderly participants were recorded using a strain gauge force platform during normal walking. The gait features were first extracted from the recorded vertical directional foot- ground reaction forces curve using PCA, and then these extracted features were adopted to develop the SVM gait classifier. The test results indicated that the performance of PCA-based SVM was on average 90% to recognize young- elderly gait patterns, resulting in a markedly improved performance over an artificial neural network-based classifier. The classification ability of the SVM with polynomial and radial basis function kernels was superior to that of the SVM with linear kernel. These results suggest that the proposed technique could provide an effective tool for gait classification in future clinical applications.     

Muscle contracture emulating system for studying artificially induced pathological gait in intact individuals

When studying pathological gait it is important to correctly identify primary gait anomalies originating from damage to the central nervous and musculoskeletal system and separate them from compensatory changes of gait pattern, which is often challenging due to the lack of knowledge related to biomechanics of pathological gait. A mechanical system consisting of specially designed trousers, special shoe arrangement, and elastic ropes attached to selected locations on the trousers and shoes is proposed to allow emulation of muscle contractures of soleus (SOL) and gastrocnemius (GAS) muscles and both SOL-GAS. The main objective of this study was to evaluate and compare gait variability as recorded in normal gait and when being constrained with the proposed system. Six neurologically and orthopedically intact volunteers walked along a 7-m walkway while gait kinematics and kinetics were recorded using VICON motion analysis system and two AMTI forceplates. Statistical analysis of coefficient of variation of kinematics and kinetics as recorded in normal walking and during the most constrained SOL-GAS condition showed comparable gait variability. Inspection of resulting group averaged gait patterns revealed considerable resemblance to a selected clinical example of spastic diplegia, indicating that the proposed mechanical system potentially represents a novel method for studying emulated pathological gait arising from artificially induced muscle contractures in neurologically intact individuals.     

Reflections on clinical gait analysis

Clinical gait analysis allows the measurement and assessment of walking biomechanics, which facilitates the identification of abnormal characteristics and the recommendation of treatment alternatives. The predominant methods for this analysis currently include the tracking of external markers placed on the patient, the monitoring of patient/ground interaction (e.g. ground reaction forces), and the recording of muscle electromyographic (EMG) activity, all during gait. These data allow the computation of stride and temporal parameters, joint/segment kinematics, joint kinetics, and EMG plots that are used to gain a better understanding of a patient's walking difficulties. Gait interpretation involves a systemic evaluation of each of these types of data, noting both corroborating and conflicting information while identifying functionally significant deviations from the normal. Understanding the etiology of these abnormalities allows the formulation of a treatment plan that may involve physical therapy, bracing, and/or surgery. This process is challenging because of the complexity of the motion, neuromuscular involvement of the patient (e.g. dynamic spasticity), variability of treatment outcome, and on occasion, uncertainty about the quality of the gait data. The experience of the interpretation team with respect to gait biomechanics, a particular patient population, and the effectiveness of different treatment modalities is the principal determinant of the success of this approach. The clinical gait analysis process continues to evolve positively. It has become more comprehensive and meaningful because of an improved understanding of normal gait biomechanics and more rigorous data collection/reduction protocols that complement accumulated clinically relevant experience.     

Joint contact stresses calculated for acetabular dysplasia patients using discrete element analysis are significantly influenced by the applied gait pattern

Gait modifications in acetabular dysplasia patients may influence cartilage contact stress patterns within the hip joint, with serious implications for clinical outcomes and the risk of developing osteoarthritis. The objective of this study was to understand how the gait pattern used to load computational models of dysplastic hips influences computed joint mechanics. Three-dimensional pre- and post-operative hip models of thirty patients previously treated for hip dysplasia with periacetabular osteotomy (PAO) were developed for performing discrete element analysis (DEA). Using DEA, contact stress patterns were calculated for each pre- and post-operative hip model when loaded with an instrumented total hip, a dysplastic, a matched control, and a normal gait pattern. DEA models loaded with the dysplastic and matched control gait patterns had significantly higher (p = 0.012 and p < 0.001) average pre-operative maximum contact stress than models loaded with the normal gait. Models loaded with the dysplastic and matched control gait patterns had nearly significantly higher (p = 0.051) and significantly higher (p = 0.008) average pre-operative contact stress, respectively, than models loaded with the instrumented hip gait. Following PAO, the average maximum contact stress for DEA models loaded with the dysplastic and matched control patterns decreased, which was significantly different (p < 0.001) from observed increases in maximum contact stress calculated when utilizing the instrumented hip and normal gait patterns. The correlation between change in DEA-computed maximum contact stress and the change in radiographic measurements of lateral center-edge angle were greatest (R² = 0.330) when utilizing the dysplastic gait pattern. These results indicate that utilizing a dysplastic gait pattern to load DEA models may be a crucial element to capturing contact stress patterns most representative of this patient population.     

Biomechanical mechanism of lateral trunk lean gait for knee osteoarthritis patients

The biomechanical mechanism of lateral trunk lean gait employed to reduce external knee adduction moment (KAM) for knee osteoarthritis (OA) patients is not well known. This mechanism may relate to the center of mass (COM) motion. Moreover, lateral trunk lean gait may affect motor control of the COM displacement. Uncontrolled manifold (UCM) analysis is an evaluation index used to understand motor control and variability of the motor task. Here we aimed to clarify the biomechanical mechanism to reduce KAM during lateral trunk lean gait and how motor variability controls the COM displacement. Twenty knee OA patients walked under two conditions: normal and lateral trunk lean gait conditions. UCM analysis was performed with respect to the COM displacement in the frontal plane. We also determined how the variability is structured with regards to the COM displacement as a performance variable. The peak KAM under lateral trunk lean gait was lower than that under normal gait. The reduced peak KAM observed was accompanied by medially shifted knee joint center, shortened distance of the center of pressure to knee joint center, and shortened distance of the knee–ground reaction force lever arm during the stance phase. Knee OA patients with lateral trunk lean gait could maintain kinematic synergy by utilizing greater segmental configuration variance to the performance variable. However, the COM displacement variability of lateral trunk lean gait was larger than that of normal gait. Our findings may provide clinical insights to effectively evaluate and prescribe gait modification training for knee OA patients.     

The importance of swing-phase initial conditions in stiff-knee gait

The diminished knee flexion associated with stiff-knee gait, a movement abnormality commonly observed in persons with cerebral palsy, is thought to be caused by an over-active rectus femoris muscle producing an excessive knee extension moment during the swing phase of gait. As a result, treatment for stiff-knee gait is aimed at altering swing-phase muscle function. Unfortunately, this treatment strategy does not consistently result in improved knee flexion. We believe this is because multiple factors contribute to stiff-knee gait. Specifically, we hypothesize that many individuals with stiff-knee gait exhibit diminished knee flexion not because they have an excessive knee extension moment during swing, but because they walk with insufficient knee flexion velocity at toe-off. We measured the knee flexion velocity at toe-off and computed the average knee extension moment from toe-off to peak flexion in 17 subjects (18 limbs) with stiff-knee gait and 15 subjects (15 limbs) without movement abnormalities. We used forward dynamic simulation to determine how adjusting each stiff-knee subject's knee flexion velocity at toe-off to normal levels would affect knee flexion during swing. We found that only one of the 18 stiff-knee limbs exhibited an average knee extension moment from toe-off to peak flexion that was larger than normal. However, 15 of the 18 limbs exhibited a knee flexion velocity at toe-off that was below normal. Simulating an increase in the knee flexion velocity at toe-off to normal levels resulted in a normal or greater than normal range of knee flexion for each of these limbs. These results suggest that the diminished knee flexion of many persons with stiff-knee gait may be caused by abnormally low knee flexion velocity at toe-off as opposed to excessive knee extension moments during swing.