Rotational and translational movement features of the pelvis and thorax during adult human locomotion

The kinematics of the pelvis and thorax are important in gait studies since their movement patterns are closely related to gait efficiency and 'smoothness' of locomotion. The purpose of this study was to identify features of normal gait patterns for later comparisons with pathological and developmental gait patterns. A two camera SELSPOT system interfaced with an HP1000 minicomputer was used to obtain three-dimensional kinematic/temporal data for the pelvis and thorax. Data from treadmill walking of eight adults were used for within subject (at different speeds) analyses. The analyses revealed a very complex pattern with a set of breakpoints which was consistent over all subjects. Some features were invariant over a range of walking speeds although the total range of motion changed considerably.     

Commercial video frame rates can produce reliable results for both normal and CP spastic gait's spatiotemporal, angular, and linear displacement variables

To investigate what sampling frequency is adequate for gait, the correlation of spatiotemporal parameters and the kinematic differences, between normal and CP spastic gait, for three sampling frequencies (100 Hz, 50 Hz, 25 Hz) were assessed. Spatiotemporal, angular, and linear displacement variables in the sagittal plane along with their 1st and 2nd derivatives were analyzed. Spatiotemporal stride parameters were highly correlated among the three sampling frequencies. The statistical model (2 × 3 ANOVA) gave no interactions between the factors group and frequency, indicating that group differences were invariant of sampling frequency. Lower frequencies led to smoother curves for all the variables, with a loss of information though, especially for the 2nd derivatives, having a homologous effect as the one of oversmoothing. It is proposed that in the circumstance that only spatiotemporal stride parameters, as well as angular and linear displacements are to be used, in gait reports, then commercial video camera speeds (25/30 Hz, 50/60 Hz when deinterlaced) can be considered as a low-cost solution to produce acceptable results.     

Optimal running speed and the evolution of hominin hunting strategies

Recent discussion of the selective pressures leading to the evolution of modern human postcranial morphology, seen as early as Homo erectus, has focused on the relative importance of walking versus running. Specifically, these conversations have centered on which gait may have been used by early Homo to acquire prey. An element of the debate is the widespread belief that quadrupeds are constrained to run at optimally efficient speeds within each gait, whereas humans are equally efficient at all running speeds. The belief in the lack of optimal running speeds in humans is based, however, on a number of early studies with experimental designs inadequate for the purpose of evaluating optimality. Here we measured the energetic cost of human running (n = 9) at six different speeds for five minutes at each speed, with careful replicates and controls. We then compared the fit of linear versus curvilinear models to the data within each subject. We found that individual humans do, in fact, have speeds at which running is significantly less costly than at other speeds (i.e., an optimal running speed). In addition, we demonstrate that the use of persistence hunting methods to gain access to prey at any running speed, even the optimum, would be extremely costly energetically, more so than a persistence hunt at optimal walking speed. We argue that neither extinct nor extant hominin populations are as flexible in the chosen speeds of persistence hunting pursuits as other researchers have suggested. Variations in the efficiency of human locomotion appear to be similar to those of terrestrial quadrupeds.     

Predicting Missing Marker Trajectories in Human Motion Data Using Marker Intercorrelations

Missing information in motion capture data caused by occlusion or detachment of markers is a common problem that is difficult to avoid entirely. The aim of this study was to develop and test an algorithm for reconstruction of corrupted marker trajectories in datasets representing human gait. The reconstruction was facilitated using information of marker inter-correlations obtained from a principal component analysis, combined with a novel weighting procedure. The method was completely data-driven, and did not require any training data. We tested the algorithm on datasets with movement patterns that can be considered both well suited (healthy subject walking on a treadmill) and less suited (transitioning from walking to running and the gait of a subject with cerebral palsy) to reconstruct. Specifically, we created 50 copies of each dataset, and corrupted them with gaps in multiple markers at random temporal and spatial positions. Reconstruction errors, quantified by the average Euclidian distance between predicted and measured marker positions, was ≤ 3 mm for the well suited dataset, even when there were gaps in up to 70% of all time frames. For the less suited datasets, median reconstruction errors were in the range 5–6 mm. However, a few reconstructions had substantially larger errors (up to 29 mm). Our results suggest that the proposed algorithm is a viable alternative both to conventional gap-filling algorithms and state-of-the-art reconstruction algorithms developed for motion capture systems. The strengths of the proposed algorithm are that it can fill gaps anywhere in the dataset, and that the gaps can be considerably longer than when using conventional interpolation techniques. Limitations are that it does not enforce musculoskeletal constraints, and that the reconstruction accuracy declines if applied to datasets with less predictable movement patterns.     

Patterns of mechanical energy change in tetrapod gait: pendula, springs and work

Kinematic and center of mass (CoM) mechanical variables used to define terrestrial gaits are compared for various tetrapod species. Kinematic variables (limb phase, duty factor) provide important timing information regarding the neural control and limb coordination of various gaits. Whereas, mechanical variables (potential and kinetic energy relative phase, %Recovery, %Congruity) provide insight into the underlying mechanisms that minimize muscle work and the metabolic cost of locomotion, and also influence neural control strategies. Two basic mechanisms identified by Cavagna et al. (1977. Am J Physiol 233:R243-R261) are used broadly by various bipedal and quadrupedal species. During walking, animals exchange CoM potential energy (PE) with kinetic energy (KE) via an inverted pendulum mechanism to reduce muscle work. During the stance period of running (including trotting, hopping and galloping) gaits, animals convert PE and KE into elastic strain energy in spring elements of the limbs and trunk and regain this energy later during limb support. The bouncing motion of the body on the support limb(s) is well represented by a simple mass-spring system. Limb spring compliance allows the storage and return of elastic energy to reduce muscle work. These two distinct patterns of CoM mechanical energy exchange are fairly well correlated with kinematic distinctions of limb movement patterns associated with gait change. However, in some cases such correlations can be misleading. When running (or trotting) at low speeds many animals lack an aerial period and have limb duty factors that exceed 0.5. Rather than interpreting this as a change of gait, the underlying mechanics of the body's CoM motion indicate no fundamental change in limb movement pattern or CoM dynamics has occurred. Nevertheless, the idealized, distinctive patterns of CoM energy fluctuation predicted by an inverted pendulum for walking and a bouncing mass spring for running are often not clear cut, especially for less cursorial species. When the kinematic and mechanical patterns of a broader diversity of quadrupeds and bipeds are compared, more complex patterns emerge, indicating that some animals may combine walking and running mechanics at intermediate speeds or at very large size. These models also ignore energy costs that are likely associated with the opposing action of limbs that have overlapping support times during walking. A recent model of terrestrial gait (Ruina et al., 2005. J Theor Biol, in press) that treats limb contact with the ground in terms of collisional energy loss indicates that considerable CoM energy can be conserved simply by matching the path of CoM motion perpendicular to limb ground force. This model, coupled with the earlier ones of pendular exchange during walking and mass-spring elastic energy savings during running, provides compelling argument for the view that the legged locomotion of quadrupeds and other terrestrial animals has generally evolved to minimize muscle work during steady level movement.     

Stance and swing phase detection during level and slope walking in the cat: effects of slope, injury, subject and kinematic detection method

In quadrupeds, there have been limited comparisons of gait timing events detection (e.g., paw contact, PC and paw-off, PO) determined from kinematics and forceplates. The goal of this study was to investigate the effect of different slopes (0, -27, +27°), recovery times after ankle extensor nerve injury and repair (2, 6, 12 weeks), subjects and detection methods on accuracy of kinematically derived PC and PO timings during feline walking. Right hindlimb kinematics and ground reaction forces (GRF) of 4 cats walking along a sloped walkway with embedded forceplates were recorded. A total of 963 walking cycles were analyzed. Gait timings were determined from five kinematic methods based on displacements, velocities or accelerations of hindlimb markers. GRF based 'gold standard' timings for PC and PO were used to determine the systematic and random error of kinematic timing. Systematic errors between the kinematic methods differed significantly (p<0.05). Methods based on vertical paw peak acceleration and velocity gave the smallest systematic errors for PC and PO, respectively. The smallest random errors (standard deviations) for PC and PO were demonstrated by method based on paw horizontal displacement relative to greater trochanter: 13.4ms and 6.6ms, respectively. Effects of slope and subject on systematic errors of kinematic methods were significant, whereas effects of recovery time after nerve injury were not. It was concluded that timing of gait events can be determined consistently using kinematics, although adjustments must be made to account for the systematic error which varies according to subject and slope condition.     

Algorithms to determine event timing during normal walking using kinematic data

Algorithms to predict heelstrike and toeoff times during normal walking using only kinematic data are presented. The accuracy of these methods was compared with the results obtained using synchronized force platform recordings of two subjects walking at a variety of speeds for a total of 12 trials. Using a 60Hz data collection system, the absolute value errors (AVE) in predicting heelstrike averaged 4.7ms, while the AVE in predicting toeoff times averaged 5.6ms. True average errors (negative for an early prediction) were +1.2ms for both heelstrike and toeoff, indicating that no systematic errors occurred. It was concluded that the proposed algorithms provide an easy and reliable method of determining event times during walking when kinematic data are collected, with a considerable improvement in resolution over visual inspection of video records, and could be utilized in conjunction with any 2-D or 3-D kinematic data collection system.     

A simple,reliable method to determine the mean gait speed using heel markers on a treadmill

Gait speed is an essential parameter of gait analysis. Our study proposed a simple and accurate method to extract a mean gait speed during walking on a treadmill using only kinematic data from markers placed on the heels of the participants’ feet. This method provided an attractive, simple method that remains resistant to errors in treadmill calibration. In addition, this method required only two markers, since heel markers are essential to gait analysis, and the proposed method is robust enough to differentiate among various gait speeds (mean error <1%).     

Speed,age, sex,and body mass index provide a rigorous basis for comparing the kinematic and kinetic profiles of the lower extremity during walking

The increased use of gait analysis has raised the need for a better understanding of how walking speed and demographic variations influence asymptomatic gait. Previous analyses mainly reported relationships between subsets of gait features and demographic measures, rendering it difficult to assess whether gait features are affected by walking speed or other demographic measures. The purpose of this study was to conduct a comprehensive analysis of the kinematic and kinetic profiles during ambulation that tests for the effect of walking speed in parallel to the effects of age, sex, and body mass index. This was accomplished by recruiting a population of 121 asymptomatic subjects and analyzing characteristic 3-dimensional kinematic and kinetic features at the ankle, knee, hip, and pelvis during walking trials at slow, normal, and fast speeds. Mixed effects linear regression models were used to identify how each of 78 discrete gait features is affected by variations in walking speed, age, sex, and body mass index. As expected, nearly every feature was associated with variations in walking speed. Several features were also affected by variations in demographic measures, including age affecting sagittal-plane knee kinematics, body mass index affecting sagittal-plane pelvis and hip kinematics, body mass index affecting frontal-plane knee kinematics and kinetics, and sex affecting frontal-plane kinematics at the pelvis, hip, and knee. These results could aid in the design of future studies, as well as clarify how walking speed, age, sex, and body mass index may act as potential confounders in studies with small populations or in populations with insufficient demographic variations for thorough statistical analyses.     

Gait selection in the ostrich: mechanical and metabolic characteristics of walking and running with and without an aerial phase

It has been argued that minimization of metabolic-energy costs is a primary determinant of gait selection in terrestrial animals. This view is based predominantly on data from humans and horses, which have been shown to choose the most economical gait (walking, running, galloping) for any given speed. It is not certain whether a minimization of metabolic costs is associated with the selection of other prevalent forms of terrestrial gaits, such as grounded running (a widespread gait in birds). Using biomechanical and metabolic measurements of four ostriches moving on a treadmill over a range of speeds from 0.8 to 6.7 m s(-1), we reveal here that the selection of walking or grounded running at intermediate speeds also favours a reduction in the metabolic cost of locomotion. This gait transition is characterized by a shift in locomotor kinetics from an inverted-pendulum gait to a bouncing gait that lacks an aerial phase. By contrast, when the ostrich adopts an aerial-running gait at faster speeds, there are no abrupt transitions in mechanical parameters or in the metabolic cost of locomotion. These data suggest a continuum between grounded and aerial running, indicating that they belong to the same locomotor paradigm.     

The relationship between joint kinetic factors and the walk-run gait transition speed during human locomotion

The primary purpose of this project was to examine whether lower extremity joint kinetic factors are related to the walk-run gait transition during human locomotion. Following determination of the preferred transition speed (PTS), each of the 16 subjects walked down a 25-m runway, and over a floor-mounted force platform at five speeds (70, 80, 90, 100, and 110% of the PTS), and ran over the force platform at three speeds (80, 100, and 120% of the PTS) while being videotaped (240 Hz) from the right sagittal plane. Two-dimensional kinematic data were synchronized with ground reaction force data (960 Hz). After smoothing, ankle and knee joint moments and powers were calculated using standard inverse dynamics calculations. The maximum dorsiflexor moment was the only variable tested that increased as walking speed increased and then decreased when gait changed to a run at the PTS, meeting the criteria set to indicate that this variable influences the walk-run gait transition during human locomotion. This supports previous research suggesting that an important factor in changing gaits at the PTS is the prevention of undue stress in the dorsiflexor muscles.     

Kinematic analysis of over-determinate biomechanical systems

In this paper, we introduce a new general method for kinematic analysis of rigid multi body systems subject to holonomic constraints. The method extends the standard analysis of kinematically determinate rigid multi body systems to the over-determinate case. This is accomplished by introducing a constrained optimisation problem with the objective function given as a function of the set of system equations that are allowed to be violated while the remaining equations define the feasible set.

We show that exact velocity and acceleration analysis can also be performed by solving linear sets of equations, originating from differentiation of the Karush–Kuhn–Tucker optimality conditions.

The method is applied to the analysis of an 18 degrees-of-freedom gait model where the kinematical drivers are prescribed with data from a motion capture experiment.

The results show that significant differences are obtained between applying standard kinematic analysis or minimising the least-square errors on the two fully equivalent 3D gait models with only the way the experimental data is processed being different.     

Validity of the MarkWiiR for kinematic analysis during walking and running gaits

The aim of this study was to validate the MarkWiiR (MW) captured by the Nintendo Wii-Remote (100-Hz) to assess active marker displacement by comparison with 2D video analysis. Ten participants were tested on a treadmill at different walking (1<6 km · h⁻¹) and running (10<13 km · h⁻¹) speeds. During the test, the active marker for MW and a passive marker for video analysis were recorded simultaneously with the two devices. The displacement of the marker on the two axes (x-y) was computed using two different programs, Kinovea 0.8.15 and CoreMeter, for the camera and MW, respectively. Pearson correlation was acceptable (x-axis r≥0.734 and y-axis r≥0.684), and Bland–Altman plots of the walking speeds showed an average error of 0.24±0.52% and 1.5±0.91% for the x- and y-axis, respectively. The difference of running speeds showed average errors of 0.67±0.33% and 1.26±0.33% for the x- and y-axes, respectively. These results demonstrate that the two measures are similar from both the x- and the y-axis perspective. In conclusion, these findings suggest that the MarkWiiR is a valid and reliable tool to assess the kinematics of an active marker during walking and running gaits.     

Error propagation from kinematic data to modeled muscle-tendon lengths during walking

Kinematic data from 3D gait analysis together with musculoskeletal modeling techniques allow the derivation of muscle-tendon lengths during walking. However, kinematic data are subject to soft tissue artifacts (STA), referring to skin marker displacements during movement. STA are known to significantly affect the computation of joint kinematics, and would therefore also have an effect on muscle-tendon lengths which are derived from the segmental positions. The present study aimed to introduce an analytical approach to calculate the error propagation from STA to modeled muscle-tendon lengths. Skin marker coordinates were assigned uncorrelated, isotropic error functions with given standard deviations accounting for STA. Two different musculoskeletal models were specified; one with the joints moving freely in all directions, and one with the joints constrained to rotation but no translation. Using reference kinematic data from two healthy boys (mean age 9 y 5 m), the propagation of STA to muscle-tendon lengths was quantified for semimembranosus, gastrocnemius and soleus. The resulting average SD ranged from 6% to 50% of the normalized muscle-tendon lengths during gait depending on the muscle, the STA magnitudes and the musculoskeletal model. These results highlight the potential impact STA has on the biomechanical analysis of modeled muscle-tendon lengths during walking, and suggest the need for caution in the clinical interpretation of muscle-tendon lengths derived from joint kinematics.     

Mechanical power in running: a comparison of different approaches

The purposes of the present study were: (1) to compare four different methods of calculating mechanical power in running on the basis of comparable data over a wide range of running velocity; (2) to examine the linearity of the relation between mechanical power as calculated with the four methods and running velocity. Eight runners participated in the investigation (height: 1.82 +/- 0.03 m, body mass: 81.05 +/- 4.69 kg). A Kistler force platform registered all components of the ground reaction force (1000 Hz) during one foot ground contact, which was additionally video taped using two high-speed video cameras running at 120 Hz. Four different methods were used to calculate mechanical power. Two methods determined the mechanical power due to the work done on the athletes' center of mass and two were calculated from the motion of the athletes' segments. The four different methods provided different relations between mechanical power and running velocity. The calculations on the basis of kinematic data cannot be recommended to determine efficiency of movement. The methods based on ground reaction force measurements revealed significant linear relations (r = 0.90, r2 = 0.84) between running velocity and mechanical power.     

Artificial neural network model for the generation of muscle activation patterns for human locomotion.

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.     

Kinematic analysis of over-determinate biomechanical systems

In this paper, we introduce a new general method for kinematic analysis of rigid multi body systems subject to holonomic constraints. The method extends the standard analysis of kinematically determinate rigid multi body systems to the over-determinate case. This is accomplished by introducing a constrained optimisation problem with the objective function given as a function of the set of system equations that are allowed to be violated while the remaining equations define the feasible set. We show that exact velocity and acceleration analysis can also be performed by solving linear sets of equations, originating from differentiation of the Karush-Kuhn-Tucker optimality conditions. The method is applied to the analysis of an 18 degrees-of-freedom gait model where the kinematical drivers are prescribed with data from a motion capture experiment. The results show that significant differences are obtained between applying standard kinematic analysis or minimising the least-square errors on the two fully equivalent 3D gait models with only the way the experimental data is processed being different.     

Determination of patient-specific multi-joint kinematic models through two-level optimization

Dynamic patient-specific musculoskeletal models have great potential for addressing clinical problems in orthopedics and rehabilitation. However, their predictive capability is limited by how well the underlying kinematic model matches the patient's structure. This study presents a general two-level optimization procedure for tuning any multi-joint kinematic model to a patient's experimental movement data. An outer level optimization modifies the model's parameters (joint position and orientations) while repeated inner level optimizations modify the model's degrees of freedom given the current parameters, with the goal of minimizing errors between model and experimental marker trajectories. The approach is demonstrated by fitting a 27 parameter, three-dimensional, 12 degree-of-freedom lower-extremity kinematic model to synthetic and experimental movement data for isolated joint (hip, knee, and ankle) and gait (full leg) motions. For noiseless synthetic data, the approach successfully recovered the known joint parameters to within an arbitrarily tight tolerance. When noise was added to the synthetic data, root-mean-square (RMS) errors between known and recovered joint parameters were within 10.4 degrees and 10 mm. For experimental data, RMS marker distance errors were reduced by up to 62% compared to methods that estimate joint parameters from anatomical landmarks. Optimized joint parameters found using a loaded full-leg gait motion differed significantly from those found using unloaded individual joint motions. In the future, this approach may facilitate the creation of dynamic patient-specific musculoskeletal models for predictive clinical applications.