Repeatability of 3D gait kinematics obtained from an electromagnetic tracking system during treadmill locomotion

The purpose of this paper was to describe a technique that enables three-dimensional (3D) gait kinematics to be obtained using an electromagnetic tracking system, and to report the intra-trial, intra-day/inter-tester and inter-day/intra-tester repeatability of kinematic gait data obtained using this technique. Ten able-bodied adults underwent four gait assessments; the same two testers tested each subject independently on two different days. Gait assessments were conducted on a custom-built long-bed treadmill with no metal components between the rollers. Each gait assessment involved familiarisation to treadmill walking, subject anatomical and functional calibration, and a period of steady-state treadmill walking at a self-selected speed. Following data collection, 3D joint kinematics were calculated using the joint coordinate system approach. 3D joint angle waveforms for 10 left and right strides were extracted and temporally normalised for each trial. Intra-trial, intra-day/inter-tester and inter-day/intra-tester repeatability of the temporally normalised kinematic waveforms were quantified using the coefficient of multiple determination (CMD). CMDs for joint kinematics averaged 0.942 intra-trial, 0.849 intra-day/inter-tester and 0.773 inter-day/intra-tester. In general, sagittal plane kinematics were more repeatable than frontal or transverse plane kinematics, and kinematics at the hip were more repeatable than at the knee or ankle. The level of repeatability of kinematic gait data obtained during treadmill walking using this protocol was equal or superior to that reported previously for overground walking using image-based protocols.     

Statistical method for prediction of gait kinematics with Gaussian process regression

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject's body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.     

Toe-out gait in patients with knee osteoarthritis partially transforms external knee adduction moment into flexion moment during early stance phase of gait: a tri-planar kinetic mechanism

Altered gait kinematics and kinetics are observed in patients with medial compartment knee osteoarthritis. Although various kinematic adaptations are proposed to be compensatory mechanisms that unload the knee, the nature of these mechanisms is presently unclear. We hypothesized that an increased toe-out angle during early stance phase of gait shifts load away from the knee medial compartment, quantified as the external adduction moment about the knee. Specifically, we hypothesized that by externally rotating the lower limb anatomy, primarily about the hip joint, toe-out gait alters the lengths of ground reaction force lever arms acting about the knee joint in the frontal and sagittal planes and transforms a portion of knee adduction moment into flexion moment. To test this hypothesis, gait data from 180 subjects diagnosed with medial compartment knee osteoarthritis were examined using two frames of reference. The first frame was attached to the tibia (reporting actual toe-out) and the second frame was attached to the laboratory (simulating no-toe-out). Four measures were compared within subjects in both frames of reference: the lengths of ground reaction force lever arms acting about the knee joint in the frontal and sagittal planes, and the adduction and flexion components of the external knee moment. The mean toe-out angle was 11.4 degrees (S.D. 7.8 degrees , range -2.2 degrees to 28.4 degrees ). Toe-out resulted in significant reductions in the frontal plane lever arm (-6.7%) and the adduction moment (-11.7%) in early stance phase when compared to the simulated no-toe-out values. These reductions were coincident with significant increases in the sagittal plane lever arm (+33.7%) and flexion moment (+25.0%). Peak adduction lever arm and moment were also reduced significantly in late stance phase (by -22.9% and -34.4%, respectively) without a corresponding increase in sagittal plane lever arm or flexion moment. These results indicate that toe-out gait in patients with medial compartment knee osteoarthritis transforms a portion of the adduction moment into flexion moment in early stance phase, suggesting that load is partially shifted away from the medial compartment to other structures.     

Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

A variety of musculoskeletal models are applied in different modelling environments for estimating muscle forces during gait. Influence of different modelling assumptions and approaches on model outputs are still not fully understood, while direct comparisons of standard approaches have been rarely undertaken. This study seeks to compare joint kinematics, joint kinetics and estimated muscle forces of two standard approaches offered in two different modelling environments (AnyBody, OpenSim). It is hypothesised that distinctive differences exist for individual muscles, while summing up synergists show general agreement. Experimental data of 10 healthy participants (28 ± 5 years, 1.72 ± 0.08 m, 69 ± 12 kg) was used for a standard static optimisation muscle force estimation routine in AnyBody and OpenSim while using two gait-specific musculoskeletal models. Statistical parameter mapping paired t-test was used to compare joint angle, moment and muscle force waveforms in Matlab. Results showed differences especially in sagittal ankle and hip angles as well as sagittal knee moments. Differences were also found for some of the muscles, especially of the triceps surae group and the biceps femoris short head, which occur as a result of different anthropometric and anatomical definitions (mass and inertia of segments, muscle properties) and scaling procedures (static vs. dynamic). Understanding these differences and their cause is crucial to operate such modelling environments in a clinical setting. Future research should focus on alternatives to classical generic musculoskeletal models (e.g. implementation of functional calibration tasks), while using experimental data reflecting normal and pathological gait to gain a better understanding of variations and divergent behaviour between approaches.     

Are clinical parameters sufficient to model gait patterns in patients with cerebral palsy using a multilinear approach?

The aim of this study was to evaluate whether clinical parameters are sufficient using, a multilinear regression model, to reproduce the sagittal plane joint angles (hip, knee, and ankle) in cerebral palsy gait. A total of 154 patients were included. The two legs were considered (308 observations). Thirty-six clinical parameters were used as regressors (range of motion, muscle strength, and spasticity of the lower). From the clinical gait analysis, the joint angles of the sagittal plane were selected. Results showed that clinical parameter does not provide sufficient information to recover joint angles and/or that the multilinear regression model is not an appropriate solution.     

The differences in sagittal plane whole-body angular momentum during gait between patients with hemiparesis and healthy people

Regulation of whole-body angular momentum (WBAM) is essential for maintaining dynamic balance during gait. Patients with hemiparesis frequently fall toward the anterior direction; however, whether this is due to impaired WBAM control in the sagittal plane during gait remains unknown. The present study aimed to investigate the differences in WBAM in the sagittal plane during gait between patients with hemiparesis and healthy individuals. Thirty-three chronic stroke patients with hemiparesis and twenty-two age- and gender-matched healthy controls walked along a 7-m walkway while gait data were recorded using a motion analysis system and force plates. WBAM and joint moment were calculated in the sagittal plane during each gait cycle. The range of WBAM in the sagittal plane in the second half of the paretic gait cycle was significantly larger than that in the first and second halves of the right gait cycle in the controls (P = 0.015 and P = 0.011). Furthermore, multiple regression analysis revealed the slower walking speed (P < 0.001) and larger knee extension moment on the non-paretic side (P = 0.003) contributed to the larger range of WBAM in the sagittal plane in the second half of the paretic gait cycle. Our findings suggest that dynamic stability in the sagittal plane is impaired in the second half of the paretic gait cycle. In addition, the large knee extension moment on the non-paretic side might play a role in the dynamic instability in the sagittal plane during gait in patients with hemiparesis.     

Foot mechanics during the first six years of independent walking

Recognition of the changes during gait that occur normally as a part of growth is essential to prevent mislabeling those changes from adult gait as evidence of gait pathology. Currently, in the literature, the definition of a mature age for ankle joint dynamics is controversial (i.e., between 5 and 10 years). Moreover, the mature age of the metatarsophalangeal (MP) joint, which is essential for the functioning of the foot, has not been defined in the literature. Thus, the objective of the present study explored foot mechanics (ankle and MP joints) in young children to define a mature age of foot function. Forty-two healthy children between 1 and 6 years of age and eight adults were measured during gait. The ground reaction force (GRF), the MP and ankle joint angles, moments, powers, and 3D angles between the joint moment and the joint angular velocity vectors (3D angle α(M.ω)) were processed and compared between four age groups (2, 3.5, 5 and adults). Based on statistical analysis, the MP joint biomechanical parameters were similar between children (older than 2 years) and adults, hinting at a quick maturation of this joint mechanics. The ankle joint parameters and the GRFs (except for the frontal plane) showed an adult-like pattern in 5-year-old children. Some ankle joint parameters, such as the joint power and the 3D angle α(M.ω) still evolved significantly until 3.5 years. Based on these results, it would appear that foot maturation during gait is fully achieved at 5 years.     

A gait index may underestimate changes of gait: a comparison of the Movement Deviation Profile and the Gait Deviation Index

The ability of the Movement Deviation Profile (MDP) and Gait Deviation Index (GDI) to detect gait changes was compared in a child with cerebral palsy who underwent game training. Conventional gait analysis showed that sagittal plane angles became mirrored about normality after training. Despite considerable gait changes, the GDI showed minimal change, while the MDP detected a difference equal to a shift between 10-9 on the Functional Assessment Questionnaire scale. Responses of the GDI and MDP were examined during a synthetic transition of the patient's curves from before intervention to a state mirrored about normality. The GDI showed a symmetric response on the two opposite sides of normality but the neural network based MDP gave an asymmetric response reflecting faithfully the unequal biomechanical consequences of joint angle changes. In conclusion, the MDP can detect altered gait even if the changes are missed by the GDI.     

Predicting timing of foot strike during running,independent of striking technique,using principal component analysis of joint angles

As 3-dimensional (3D) motion-capture for clinical gait analysis continues to evolve, new methods must be developed to improve the detection of gait cycle events based on kinematic data. Recently, the application of principal component analysis (PCA) to gait data has shown promise in detecting important biomechanical features. Therefore, the purpose of this study was to define a new foot strike detection method for a continuum of striking techniques, by applying PCA to joint angle waveforms. In accordance with Newtonian mechanics, it was hypothesized that transient features in the sagittal-plane accelerations of the lower extremity would be linked with the impulsive application of force to the foot at foot strike. Kinematic and kinetic data from treadmill running were selected for 154 subjects, from a database of gait biomechanics. Ankle, knee and hip sagittal plane angular acceleration kinematic curves were chained together to form a row input to a PCA matrix. A linear polynomial was calculated based on PCA scores, and a 10-fold cross-validation was performed to evaluate prediction accuracy against gold-standard foot strike as determined by a 10 N rise in the vertical ground reaction force. Results show 89–94% of all predicted foot strikes were within 4 frames (20 ms) of the gold standard with the largest error being 28 ms. It is concluded that this new foot strike detection is an improvement on existing methods and can be applied regardless of whether the runner exhibits a rearfoot, midfoot, or forefoot strike pattern.     

The efficacy of a video-based marker-less tracking system for gait analysis

Most clinical gait analyses are conducted using motion capture systems which track retro-reflective markers that are placed on key landmarks of the participants. An alternative to a three-dimensional (3D) motion capture, marker-based, optical camera system may be a marker-less video-based tracking system. The aim of our study was to investigate the efficacy of the use of a marker-less tracking system in the calculation of 3D joint angles for possible use in clinical gait analysis. Ten participants walked and jogged on a treadmill and their kinematic data were captured with a marker and marker-less tracking system simultaneously. The hip, knee and ankle angles in the frontal, sagittal and transverse planes were computed. Root Mean Square differences (RMS_diff) between corresponding angles for each participant’s support phase were calculated and averaged to derive the mean within-subject RMS_diff. These within-subject means were averaged to obtain the mean between-subject RMS_diff for the relevant joint angles in the two gait conditions (walking and jogging). The RMS_diff between the two tracking systems was less than 1° for all rotations of the three joint angles of the hip and knee. However, there were slightly larger differences in the ankle joint angles. The results of this study suggest a potential application in gait analysis in clinical settings where observations of anatomical motions may provide meaningful feedback.     

Comparison of hierarchical and six degrees-of-freedom marker sets in analyzing gait kinematics

The objective of this study was to determine how marker spacing, noise, and joint translations affect joint angle calculations using both a hierarchical and a six degrees-of-freedom (6DoF) marker set. A simple two-segment model demonstrates that a hierarchical marker set produces biased joint rotation estimates when sagittal joint translations occur whereas a 6DoF marker set mitigates these bias errors with precision improving with increased marker spacing. These effects were evident in gait simulations where the 6DoF marker set was shown to be more accurate at tracking axial rotation angles at the hip, knee, and ankle.     

Human Gait Recognition Using Hip,Knee and Ankle Joint Ratios

The estimation of joint angle ratios for healthy and afflicted subjects in characterizing the human gait has great significance in the development of limb prosthetics. The two dimensional analysis of human gait was performed and the ratio of hip to knee, knee to ankle, hip to ankle as well as the time taken for achieving a gait were determined. The percentage of affliction was computed based on the joint angle ratios and comparison was made with healthy gait. The joint ratios were fed as input to the driving system which comprises of six DC motors for the positioning of knee, hip and ankle during gait. Then different control strategies like P, PI and PID were tested. The t-test and ANOVA analysis were conducted between healthy, afflicted and PID controller to determine the significant difference between their joint angle ratios. The estimation of joint angle ratio improved the accuracy of the control system drive (desired position of knee, hip and ankle motors). The presence of oscillations in the output response was reduced for P and PI controllers. The implementation of PID controller eliminated the presence of peak overshoot and more settling time. Thus the joint angle ratio provides the best possible assistance to the disabled persons by appropriately compensating the affliction.     

Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors

Walking is one of the fundamental motor tasks executed during aquatic therapy. Previous kinematics analyses conducted using waterproofed video cameras were limited to the sagittal plane and to only one or two consecutive steps. Furthermore, the set-up and post-processing are time-consuming and thus do not allow a prompt assessment of the correct execution of the movements during the aquatic session therapy. The aim of the present study was to estimate the 3D joint kinematics of the lower limbs and thorax-pelvis joints in sagittal and frontal planes during underwater walking using wearable inertial and magnetic sensors. Eleven healthy adults were measured during walking both in shallow water and in dry-land conditions. Eight wearable inertial and magnetic sensors were inserted in waterproofed boxes and fixed to the body segments by means of elastic modular bands. A validated protocol (Outwalk) was used. Gait cycles were automatically segmented and selected if relevant intraclass correlation coefficients values were higher than 0.75. A total of 704 gait cycles for the lower limb joints were normalized in time and averaged to obtain the mean cycle of each joint, among participants. The mean speed in water was 40% lower than that of the dry-land condition. Longer stride duration and shorter stride distance were found in the underwater walking. In the sagittal plane, the knee was more flexed (≈ 23°) and the ankle more dorsiflexed (≈ 9°) at heel strike, and the hip was more flexed at toe-off (≈ 13°) in water than on land. On the frontal plane in the underwater walking, smoother joint angle patterns were observed for thorax-pelvis and hip, and ankle was more inversed at toe-off (≈ 7°) and showed a more inversed mean value (≈ 7°). The results were mainly explained by the effect of the speed in the water as supported by the linear mixed models analysis performed. Thus, it seemed that the combination of speed and environment triggered modifications in the joint angles in underwater gait more than these two factors considered separately. The inertial and magnetic sensors, by means of fast set-up and data analysis, can supply an immediate gait analysis report to the therapist during the aquatic therapy session.     

Validity of time series kinematical data as measured by a markerless motion capture system on a flatland for gait assessment

As a cost-effective, clinician-friendly gait assessment tool, the Kinect v2 sensor may be effective for assessing lower extremity joint kinematics. This study aims to examine the validity of time series kinematical data as measured by the Kinect v2 on a flatland for gait assessment. In this study, 51 healthy subjects walked on a flatland while kinematic data were extracted concurrently using the Kinect and Vicon systems. The kinematic outcomes comprised the hip and knee joint angles. Parallel translation of Kinect data obtained throughout the gait cycle was performed to minimize the differences between the Kinect and Vicon data. The ensemble curves of the hip and knee joint angles were compared to investigate whether the Kinect sensor can consistently and accurately assess lower extremity joint motion throughout the gait cycle. Relative consistency was assessed using Pearson correlation coefficients. Joint angles measured by the Kinect v2 followed the trend of the trajectories made by the Vicon data in both the hip and knee joints in the sagittal plane. The trajectories of the hip and knee joint angles in the frontal plane differed between the Kinect and Vicon data. We observed moderate to high correlation coefficients of 20%–60% of the gait cycle, and the largest difference between Kinect and Vicon data was 4.2°. Kinect v2 time series kinematical data obtained on the flatland are validated if the appropriate correction procedures are performed. Future studies are warranted to examine the reproducibility and systematic bias of the Kinect v2.     

Influence of walking speed on gait parameters

Modern three-dimensional gait analysis systems give information on joint angles and moments in the sagittal and coronal planes, for which normal ranges may not be readily available in the literature. Since patients with joint disease tend to walk slowly and with a short stride, it is essential that normal ranges for gait parameters should be defined with reference to speed of walking. This we have done using a population of 10 normal male subjects agea from 18 to 63 years, walking at speeds which range from very slow to very fast. The ranges of knee angle and moment are given, together with the changes in these parameters with walking speed. Peak knee flexion moment is strongly related to walking speed, whereas coronal plane knee angle is virtually independent of it. The stride length is probably the best basis for deciding the normal range for a particular measurement.     

Double-step registration of in vivo stereophotogrammetry with both in vitro 6-DOFs electrogoniometry and CT medical imaging

Standard registration techniques of bone morphology to motion analysis data often lead to unsatisfactory motion simulation because of discrepancies during the location of anatomical landmarks in the datasets. This paper describes an iterative registration method of a three-dimensional (3D) skeletal model with both 6 degrees-of-freedom joint kinematics and standard motion analysis data. The method is demonstrated in this paper on the lower limb. The method includes two steps. A primary registration allowed synchronization of in vitro kinematics of the knee and ankle joints using flexion/extension angles from in vivo gait analysis. Results from primary registration were then improved by a so-called advanced registration, which integrated external constraints obtained from experimental gait pre-knowledge. One cadaver specimen was analyzed to obtain both joint kinematics of knee and ankle joints using 3D electrogoniometry, and 3D bone morphology from medical imaging data. These data were registered with motion analysis data from a volunteer during the execution of locomotor tasks. Computer graphics output was implemented to visualize the results for a motion of sitting on a chair. Final registration results allowed the observation of both in vivo motion data and joint kinematics from the synchronized specimen data. The method improved interpretation of gait analysis data, thanks to the combination of realistic 3D bone models and joint mechanism. This method should be of interest both for research in gait analysis and medical education. Validation of the overall method was performed using RMS of the differences between bone poses estimated after registration and original data from motion analysis.     

The influence of sagittal center of pressure offset on gait kinematics and kinetics

ObjectivesKinetic patterns of the lower extremity joints have been shown to be influenced by modification of the location of the center of pressure (CoP) of the foot. The accepted theory is that a shifted location of the CoP alters the distance between the ground reaction force and the center of the joint, thereby modifying torques during gait. Various footwear designs have been reported to significantly alter the magnitude of sagittal joint torques during gait. However, the relationship between the CoP and the kinetic patterns in the sagittal plane has not been examined. The aim of this study was to evaluate the association between the sagittal location of the CoP and gait patterns during gait in healthy men.MethodsA foot-worn biomechanical device which allows controlled manipulation of the CoP location was utilized. Fourteen healthy men underwent successive gait analysis with the device set to convey three different sagittal locations of the CoP: neutral, anterior offset and posterior offset.ResultsCoP translation in the sagittal plane (i.e., from posterior to anterior) significantly related with an ankle dorsiflexion torque and a knee extension torque shift throughout the stance phase. Likewise, an anterior translation of the CoP significantly reduced the extension torque at the hip during pre-swing.ConclusionsThe study results confirm a direct correlation between sagittal offset of the CoP and the magnitude of joint torques throughout the lower extremity.     

Presenting joint kinematics of human locomotion using phase plane portraits and Poincaré maps

Additional graphical tools are needed to better visualize the joint kinematics of human locomotion. Standard plots in which the joint displacements are plotted against time or percent gait cycle do not provide sufficient information about the dynamics of the system. In this article, a study based on the two graphical tools of nonlinear dynamics to visualize the steady-state kinematics of human gait is presented. An experimental setup was developed to acquire the necessary data for application of the techniques. Twenty young adults, whose medical histories are free of gait pathology, were tested. Computerized electrogoniometers and foot switches were used to measure the kinematic data of the lower extremities and capture four instants of the gait cycle: heel strike, foot flat, heel off, and toe off. Phase plane portraits of each joint were constructed for the sagittal plane by plotting angular velocity against angular displacement. Poincaré maps were obtained by periodically sampling the joint profiles at toe off and plotting the ith iterate against the (i + 1)th one. Phase plane portraits are useful in monitoring the variations of joint velocity and position on the same graph in a more compact form. Poincaré maps are effective in differentiating steady gait from transient locomotion.     

Speed-dependent reference joint trajectory generation for robotic gait support

For the control of actuated orthoses, or gait rehabilitation robotics, kinematic reference trajectories are often required. These trajectories, consisting of joint angles, angular velocities and accelerations, are highly dependent on walking-speed. We present and evaluate a novel method to reconstruct body-height and speed-dependent joint trajectories. First, we collected gait kinematics in fifteen healthy (middle) aged subjects (47–68), at a wide range of walking-speeds (0.5–5 kph). For each joint trajectory multiple key-events were selected (among which its extremes). Second, we derived regression-models that predict the timing, angle, angular velocity and acceleration for each key-event, based on walking-speed and the subject?s body-height. Finally, quintic splines were fitted between the predicted key-events to reconstruct a full gait cycle. Regression-models were obtained for hip ab-/adduction, hip flexion/extension, knee flexion/extension and ankle plantar-/dorsiflexion. Results showed that the majority of the key-events were dependent on walking-speed, both in terms of timing and amplitude, whereas the body-height had less effect. The reconstructed trajectories matched the measured trajectories very well, in terms of angle, angular velocity and acceleration. For the angles the RMSE between the reconstructed and measured trajectories was 2.6°. The mean correlation coefficient between the reconstructed and measured angular trajectories was 0.91. The method and the data presented in this paper can be used to generate speed-dependent gait patterns. These patterns can be used for the control of several robotic gait applications. Alternatively they can assist the assessment of pathological gait, where they can serve as a reference for “normal” gait.