Spatia Visualisation of Statistically Processed Gait Data
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A novel procedure for presentation and visualization of gait data has been presented. Values of three main angles (hip, knee and ankle) have been measured in five gait phases on the population of 20 students. After statistical processing for each angle in particular gait phase, ellipsoids containing 80% probability in the distribution has been determined using multivariate normal distribution. Gait normality has been discussed according to the relation of the measured angles and ellipsoid volumes for each phase.Keywords:
Ellipsoid
Gait cycle
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ObjectiveTo study normal gait characteristics of healthy adults in different age groups.Methods90 healthy adults were divided into three groups according to their ages. It was 20～39 year group, 40～59 year group and 60～70 year group. They received a gait analysis with the gait analysis system based on digital video and image processing which could provide temporal-spatial parameters and kinematic parameters. The gait data of the three groups were compared. Relationships between gait speed and other gait parameters were investigated.ResultsThere were statistically significant differences in some parameters among three groups. Gait speed was significantly correlated with stride time, stride length, stance time (%), cadence, maximum flexion of hip and knee in swing phase.ConclusionThe normal gait patterns of healthy adult established with the gait analysis system based on digital video and image processing can be used as the base line to compare with abnormal gait.
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The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from three-dimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.
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The purpose is to investigate the validity of an inertial-sensor based gait analysis system (InvestiGAIT) consisting
of off-the-shelf sensors and an in-house capturing and analyzing software. The gait of five persons with
transfermoral limb loss were captured with the inertial system (Shimmer sensors) and the motion capture system
(Vicon) integrating two force plates chosen as reference system in this study. Eleven gait parameters are
determined from the data of the captured gait sequences. These gait parameters were compared descriptively
and statistically using boxplots, Bland-Altman-plots, including the mean of difference (MOD) and the limits
of agreement (LoA), the standard error of the mean (SEM), the Wilcoxon test and the Pearsonâs correlation
coefficient. A complete validity of the gait parameters was not assumed due to the different measurement
methods and the impact of the IMU sensor attachment (on the lower shank above the ankle). For the sound
and the amputated leg four gait parameters show no significant difference (stride duration, cadence, velocity,
stride length). All the other parameters have a p-value smaller than 0.05. Most of the gait parameters have
a small MOD, SEM and LoA. These values show a very small absolute difference between the gait parameters
of both systems. Based on the results the InvestiGAIT system can be assumed as valid and suitable for
follow-up investigations of human gait in research projects or the clinical environment. Nevertheless, further
investigations with healthy subjects and a sensor attachment on the subjectsâ shoe are planned.
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This research highlights the results obtained from applying the method of inverse kinematics, using Groebner basis theory, to the human gait cycle to extract and identify lower extremity gait signatures. The increased threat from suicide bombers and the force protection issues of today have motivated a team at Air Force Institute of Technology (AFIT) to research pattern recognition in the human gait cycle. The purpose of this research is to identify gait signatures of human subjects and distinguish between subjects carrying a load to those subjects without a load. These signatures were investigated via a model of the lower extremities based on motion capture observations, in particular, foot placement and the joint angles for subjects affected by carrying extra load on the body. The human gait cycle was captured and analyzed using a developed toolkit consisting of an inverse kinematic motion model of the lower extremity and a graphical user interface. Hip, knee, and ankle angles were analyzed to identify gait angle variance and range of motion. Female subjects exhibited the most knee angle variance and produced a proportional correlation between knee flexion and load carriage.
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The human gait analysis can provide important data for gait patterns classification of individuals as input for identification of persons according to gait parameters. The set of gait parameters that are relevant for identification purposes is not defined yet. Therefore our recent research target is to distinguish similarities and differences, evaluate the variability of gait parameters of the relevant group of people and conclude the usability for identification purposes. The gait parameters of ten subjects were recorded, calculated and evaluated in our Human motion analysis laboratory at the Technical University of Kosice. We use SMART system to capture motion data - dynamic parameters describing trajectories of 25 markers. Their positions, velocities and accelerations provide input for further calculation and linear analysis of human gait as joint angles, rotation angles, step frequency, length and with of step, gait cycle phases.
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With the rise of biofeedback in gait training in cerebral palsy there is a need for real-time measurements of gait kinematics. The Human Body Model (HBM) is a recently developed model, optimized for the real-time computing of kinematics. This study evaluated differences between HBM and two commonly used models for clinical gait analysis: the Newington Model, also known as Plug-in-Gait (PiG), and the calibrated anatomical system technique (CAST). Twenty-five children with cerebral palsy participated. 3D instrumented gait analyses were performed in three laboratories across Europe, using a comprehensive retroreflective marker set comprising three models: HBM, PiG and CAST. Gait kinematics from the three models were compared using statistical parametric mapping, and RMSE values were used to quantify differences. The minimal clinically significant difference was set at 5°. Sagittal plane differences were mostly less than 5°. For frontal and transverse planes, differences between all three models for almost all segment and joint angles exceeded the value of minimal clinical significance. Which model holds the most accurate information remains undecided since none of the three models represents a ground truth. Meanwhile, it can be concluded that all three models are equivalent in representing sagittal plane gait kinematics in clinical gait analysis.
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