A study of gait acceleration and synchronisation in healthy adult subjects

Accelerometry-based gait analysis is widely recognised as a promising tool in healthcare and clinical settings since it is unobtrusive, inexpensive and capable of providing insightful information on human gait characteristics. In order to expand the application of this technology in daily environments, it is desirable to develop reliable gait measures and their extraction methods from the acceleration signal that can differentiate between normal and atypical gait. Important examples of such measures are gait cycle and gait-induced acceleration magnitude, which are known to be closely related to each other depending on each individual's physical condition. In this study, we derive a model equation with two parameters which captures the essential relationships between gait cycle and gait acceleration based on experiments and physical modelling. We also introduce as a new gait parameter a set of indexes to evaluate the synchronisation behaviour of gait timing. The function and utility of the proposed parameters are examined in 11 healthy subjects during walking under various selected conditions.     

The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm. In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm’s gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment.     

Classification of gait patterns in the time-frequency domain

This paper describes the classification of gait patterns among descending stairs, ascending stairs and level walking activities using accelerometers arranged in antero-posterior and vertical direction on the shoulder of a garment. Gait patterns in continuous accelerometer records were classified in two steps. In the first step, direct spatial correlation of discrete dyadic wavelet coefficients was applied to separate the segments of gait patterns in the continuous accelerometer record. Compared to the reference system, averaged absolute error 0.387 s for ascending stairs and 0.404 s for descending stairs were achieved. The overall sensitivity and specificity of ascending stairs were 98.79% and 99.52%, and those of descending stairs were 97.35% and 99.62%. In the second step, powers of wavelet coefficients of 2 s time duration from separated segments of vertical and antero-posterior acceleration signals were used as features in classification. Our results proved a reliable technique of measuring gait patterns during physical activity.     

Gait and posture discrimination in sheep using a tri-axial accelerometer

Temporo-spatial observation of the leg could provide important information about the general condition of an animal, especially for those such as sheep and other free-ranging farm animals that can be difficult to access. Tri-axial accelerometers are capable of collecting vast amounts of data for locomotion and posture observations; however, interpretation and optimization of these data records remain a challenge. The aim of the present study was to introduce an optimized method for gait (walking, trotting and galloping) and posture (standing and lying) discrimination, using the acceleration values recorded by a tri-axial accelerometer mounted on the hind leg of sheep. The acceleration values recorded on the vertical and horizontal axes, as well as the total acceleration values were categorized. The relative frequencies of the acceleration categories (RFACs) were calculated in 3-s epochs. Reliable RFACs for gait and posture discrimination were identified with discriminant function and canonical analyses. Post hoc predictions for the two axes and total acceleration were conducted, using classification functions and classification scores for each epoch. Mahalanobis distances were used to determine the level of accuracy of the method. The highest discriminatory power for gait discrimination yielded four RFACs on the vertical axis, and five RFACs each on the horizontal axis and total acceleration vector. Classification functions showed the highest accuracy for walking and galloping. The highest total accuracy on the vertical and horizontal axes were 90% and 91%, respectively. Regarding posture discrimination, the vertical axis exhibited the highest discriminatory power, with values of RFAC (0, 1]=99.95% for standing; and RFAC (−1, 0]=99.50% for lying. The horizontal axis showed strong discrimination for the lying side of the animal, as values were in the acceleration category of (0, 1] for lying on the left side and (−1, 0] on the right side. The algorithm developed by the method employed in the present study facilitates differentiation of the various types of gait and posture in animals from fewer data records, and produces the most reliable acceleration values from only one axis within a short time frame. The present study introduces an optimized method by which the tri-axial accelerometer can be used in gait and posture discrimination in sheep as an animal model.     

Reliability of segmental accelerations measured using a new wireless gait analysis system

The purpose of this study was to determine the inter- and intra-examiner reliability, and stride-to-stride reliability, of an accelerometer-based gait analysis system which measured 3D accelerations of the upper and lower body during self-selected slow, preferred and fast walking speeds. Eight subjects attended two testing sessions in which accelerometers were attached to the head, neck, lower trunk, and right shank. In the initial testing session, two different examiners attached the accelerometers and performed the same testing procedures. A single examiner repeated the procedure in a subsequent testing session. All data were collected using a new wireless gait analysis system, which features near real-time data transmission via a Bluetooth network. Reliability for each testing condition (4 locations, 3 directions, 3 speeds) was quantified using a waveform similarity statistic known as the coefficient of multiple determination (CMD). CMD's ranged from 0.60 to 0.98 across all test conditions and were not significantly different for inter-examiner (0.86), intra-examiner (0.87), and stride-to-stride reliability (0.86). The highest repeatability for the effect of location, direction and walking speed were for the shank segment (0.94), the vertical direction (0.91) and the fast walking speed (0.91), respectively. Overall, these results indicate that a high degree of waveform repeatability was obtained using a new gait system under test-retest conditions involving single and dual examiners. Furthermore, differences in acceleration waveform repeatability associated with the reapplication of accelerometers were small in relation to normal motor variability.     

Segmented Bayesian calibration approach for estimating age in forensic science

Forensic age estimation is receiving growing attention from researchers in the last few years. Accurate estimates of age are needed both for identifying real age in individuals without any identity document and assessing it for human remains. The methods applied in such context are mostly based on radiological analysis of some anatomical districts and entail the use of a regression model. However, estimating chronological age by regression models leads to overestimated ages in younger subjects and underestimated ages in older ones. We introduced a full Bayesian calibration method combined with a segmented function for age estimation that relied on a Normal distribution as a density model to mitigate this bias. In this way, we were also able to model the decreasing growth rate in juveniles. We compared our new Bayesian‐segmented model with other existing approaches. The proposed method helped producing more robust and precise forecasts of age than compared models while exhibited comparable accuracy in terms of forecasting measures. Our method seemed to overcome the estimation bias also when applied to a real data set of South‐African juvenile subjects.     

Wireless Tri-Axial Trunk Accelerometry Detects Deviations in Dynamic Center of Mass Motion Due to Running-Induced Fatigue

Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18–25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorrelation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p < .05), decreased the anteroposterior step regularity (p < .05), and increased the anteroposterior sample entropy (p < .05) of trunk accelerometry patterns. Our findings indicate that treadmill running-induced fatigue might reveal itself in a greater contribution of variability in horizontal plane trunk accelerations, with anteroposterior trunk accelerations that are less regular from step-to-step and are less predictable. It appears that trunk accelerometry parameters can be used to detect deviations in dynamic CoM motion induced by treadmill running fatigue, yet it is unknown how robust or generalizable these parameters are to outdoor running environments.     

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.     

Phase-dependent modulation of corticospinal excitability during the observation of the initial phase of gait

This study was undertaken to identify the temporal characteristics of corticospinal excitability of tibialis anterior muscle during the observation of the initial phase of gait. For this purpose, using transcranial magnetic stimulation, we recorded motor evoked potentials (MEPs) during the observation of the second step of an actor’s first three steps of gait initiation with (complex gait) or without (normal gait) an obstacle and unstable surface. The results demonstrate that (1) MEPs during the observation of the initial phase of normal gait were significantly increased only at early swing phase, but not other phases (mid-swing, heel contact, mid-stance, and heel off) and (2) MEPs during the observation of the initial phase of complex gait were significantly increased at early swing and also at mid-swing and heel contact phases. These findings provide the first evidence that corticospinal excitability during the observation of gait, especially the initial phase, is modulated in phase- and motor-demanded-dependent manners.     

A computational model for dynamic analysis of the human gait

Biomechanical models are important tools in the study of human motion. This work proposes a computational model to analyse the dynamics of lower limb motion using a kinematic chain to represent the body segments and rotational joints linked by viscoelastic elements. The model uses anthropometric parameters, ground reaction forces and joint Cardan angles from subjects to analyse lower limb motion during the gait. The model allows evaluating these data in each body plane. Six healthy subjects walked on a treadmill to record the kinematic and kinetic data. In addition, anthropometric parameters were recorded to construct the model. The viscoelastic parameter values were fitted for the model joints (hip, knee and ankle). The proposed model demonstrated that manipulating the viscoelastic parameters between the body segments could fit the amplitudes and frequencies of motion. The data collected in this work have viscoelastic parameter values that follow a normal distribution, indicating that these values are directly related to the gait pattern. To validate the model, we used the values of the joint angles to perform a comparison between the model results and previously published data. The model results show a same pattern and range of values found in the literature for the human gait motion.     

Impact of the method of exposure in total hip arthroplasty on the variability of gait in the first 6 months of the postoperative period

Introduction, objectiveGait analysis has provided important information about the variability of gait for patients prior to and after total hip arthroplasty (THA). The objective of this research was to clarify how the method of exposure in total hip arthroplasty affects the variability of gait.Materials and methodGait analysis was performed at 0.8 m/s, 1.0 m/s, and 1.2 m/s on 25 patients with direct-lateral exposure (DL), 22 with antero-lateral exposure (AL) and 25 with posterior exposure (P) during total hip arthroplasty. The control group was represented by 45 healthy subjects of identical age. Gait analysis was performed pre-operatively and 3 and 6 months after the surgery. Gait parameter variability was characterized by the coefficient of variance (CV) of spatial–temporal parameters and by the mean coefficient of variance (MeanCV) of angular parameters.ResultsThe variability of gait tends to reach control values during the first 6 months of the postoperative period in all three patient groups. Six months after THA, in patients operated with DL and AL exposure the variability of gait differs significantly from control values; however, in patients operated with P exposure, the variability of spatial–temporal and angular parameters – except the rotation of pelvis – was similar to that of controls.Discussion, conclusionThe type of surgical technique significantly influences the variability of gait. Difference in the variability of angular parameters predicts gait instability and increased risk of falling after THA without the joint capsule preserved. Joint capsule preservation ensures a recovery of gait variability. It should be taken into account when compiling rehabilitation protocols. Differences related to the method of exposure should be considered when abandoning therapeutic aids.     

Bayesian state space models for inferring and predicting temporal gene expression profiles

Prediction of gene dynamic behavior is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. Unfortunately, most existing techniques used for the inclusion of the temporal correlations treat the time course as evenly distributed time intervals and use stationary models with time-invariant settings. This is an assumption that is often violated in microarray time course data since the time course expression data are at unequal time points, where the difference in sampling times varies from minutes to days. Furthermore, the unevenly spaced short time courses with sudden changes make the prediction of genetic dynamics difficult. In this paper, we develop two types of Bayesian state space models to tackle this challenge for inferring and predicting the gene expression profiles associated with diseases. In the univariate time-varying Bayesian state space models we treat both the stochastic transition matrix and the observation matrix time-variant with linear setting and point out that this can easily be extended to nonlinear setting. In the multivariate Bayesian state space model we include temporal correlation structures in the covariance matrix estimations. In both models, the unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters and hidden variables. We apply our models to multiple tissue polygenetic affymetrix data sets. Results show that the predictions of the genomic dynamic behavior can be well captured by the proposed models.     

Classification of broiler behaviours using triaxial accelerometer and machine learning

Understanding broiler behaviours provides important implications for animal well-being and farm management. The objectives of this study were to classify specific broiler behaviours by analysing data from wearable accelerometers using two machine learning models, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). Lightweight triaxial accelerometers were used to record accelerations of nine 7-week-old broilers at a sampling frequency of 40 Hz. A total of 261.6-min data were labelled for four behaviours – walking, resting, feeding and drinking. Instantaneous motion features including magnitude area, vector magnitude, movement variation, energy, and entropy were extracted and stored in a dataset which was then segmented by one of the six window lengths (1, 3, 5, 7, 10 and 20 s) with 50% overlap between consecutive windows. The mean, variation, SD, minimum and maximum of each instantaneous motion feature and two-way correlations of acceleration data were calculated within each window, yielding a total of 43 statistic features for training and testing of machine learning models. Performance of the models was evaluated using pure behaviour datasets (single behaviour type per dataset) and continuous behaviour datasets (continuous recording that involved multiple behaviour types per dataset). For pure behaviour datasets, both KNN and SVM models showed high sensitivities in classifying broiler resting (87% and 85%, respectively) and walking (99% and 99%, respectively). The accuracies of SVM were higher than KNN in differentiating feeding (88% and 75%, respectively) and drinking (83% and 62%, respectively) behaviours. Sliding window with 1-s length yielded the best performance for classifying continuous behaviour datasets. The performance of classification model generally improved as more birds were included for training. In conclusion, classification of specific broiler behaviours can be achieved by recording bird triaxial accelerations and analysing acceleration data through machine learning. Performances of different machine learning models differ in classifying specific broiler behaviours.     

Integrated analysis of gait parameters and gene expression profiles in a murine model of subarachnoid hemorrhage

Gait analysis has been widely used to examine the behavioral presentation of numerous neurological disorders. Thorough murine model evaluation of the subarachnoid hemorrhage (SAH)-associated gait deficits is missing. This study measures gait deficits using a clinically relevant murine model of SAH to examine associations between gait variability and SAH-associated gene expressions. A total of 159 dynamic and static gait parameters from the endovascular perforation murine model for simulating clinical human SAH were determined using the CatWalk system. Eighty gait parameters and the mRNA expression levels of 35 of the 88 SAH-associated genes were differentially regulated in the diseased models. Totals of 42 and 38 gait parameters correlated with the 35 SAH-associated genes positively and negatively with Pearson's correlation coefficients of >0.7 and <−0.7, respectively. p-SP1⁴⁵³ expression in the motor cortex in SAH animal models displays a significant correlation with a subset of gait parameters associated with muscular strength and coordination of limb movements. Our data highlights a strong correlation between gait variability and SAH-associated gene expression. p-SP1⁴⁵³ expression could act as a biomarker to monitor SAH pathological development and a therapeutic target for SAH.     

Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

Clinical gait analysis has proven to reduce uncertainties in selecting the appropriate quantity and type of treatment for patients with neuromuscular disorders. However, gait analysis as a clinical tool is under-utilised due to the limitations and cost of acquiring and managing data. To overcome these obstacles, inertial motion capture (IMC) recently emerged to counter the limitations attributed to other methods. This paper investigates the use of IMC for training and testing a back-propagation artificial neural network (ANN) for the purpose of distinguishing between hemiparetic stroke and able-bodied ambulation. Routine gait analysis was performed on 30 able-bodied control subjects and 28 hemiparetic stroke patients using an IMC system. An ANN was optimised to classify the two groups, achieving a repeatable network accuracy of 99.4%. It is concluded that an IMC system and appropriate computer methods may be useful for the planning and monitoring of gait rehabilitation therapy of stroke victims.     

Identifiability of parameters in MCMC Bayesian inference of phylogeny

Methods for Bayesian inference of phylogeny using DNA sequences based on Markov chain Monte Carlo (MCMC) techniques allow the incorporation of arbitrarily complex models of the DNA substitution process, and other aspects of evolution. This has increased the realism of models, potentially improving the accuracy of the methods, and is largely responsible for their recent popularity. Another consequence of the increased complexity of models in Bayesian phylogenetics is that these models have, in several cases, become overparameterized. In such cases, some parameters of the model are not identifiable; different combinations of nonidentifiable parameters lead to the same likelihood, making it impossible to decide among the potential parameter values based on the data. Overparameterized models can also slow the rate of convergence of MCMC algorithms due to large negative correlations among parameters in the posterior probability distribution. Functions of parameters can sometimes be found, in overparameterized models, that are identifiable, and inferences based on these functions are legitimate. Examples are presented of overparameterized models that have been proposed in the context of several Bayesian methods for inferring the relative ages of nodes in a phylogeny when the substitution rate evolves over time.     

On the potential of lower limb muscles to accelerate the body's centre of mass during walking

Quantification of lower limb muscle function during gait or other common activities may be achieved using an induced acceleration analysis, which determines the contributions of individual muscles to the accelerations of the body's centre of mass. However, this analysis is reliant on a mathematical optimisation for the distribution of net joint moments among muscles. One approach that overcomes this limitation is the calculation of a muscle's potential to accelerate the centre of mass based on either a unit-force or maximum-activation assumption. Unit-force muscle potential accelerations are determined by calculating the accelerations induced by a 1 N muscle force, whereas maximum-activation muscle potential accelerations are determined by calculating the accelerations induced by a maximally activated muscle. The aim of this study was to describe the acceleration potentials of major lower limb muscles during normal walking obtained from these two techniques, and to evaluate the results relative to absolute (optimisation-based) muscle-induced accelerations. Dynamic simulations of walking were generated for 10 able-bodied children using musculoskeletal models, and potential- and absolute induced accelerations were calculated using a perturbation method. While the potential accelerations often correctly identified the major contributors to centre-of-mass acceleration, they were noticeably different in magnitude and timing from the absolute induced accelerations. Potential induced accelerations predicted by the maximum-activation technique, which accounts for the force-generating properties of muscle, were no more consistent with absolute induced accelerations than unit-force potential accelerations. The techniques described may assist treatment decisions through quantitative analyses of common gait abnormalities and/or clinical interventions.     

Novel approach to ambulatory assessment of human segmental orientation on a wearable sensor system

A new method using a double-sensor difference based algorithm for analyzing human segment rotational angles in two directions for segmental orientation analysis in the three-dimensional (3D) space was presented. A wearable sensor system based only on triaxial accelerometers was developed to obtain the pitch and yaw angles of thigh segment with an accelerometer approximating translational acceleration of the hip joint and two accelerometers measuring the actual accelerations on the thigh. To evaluate the method, the system was first tested on a 2° of freedom mechanical arm assembled out of rigid segments and encoders. Then, to estimate the human segmental orientation, the wearable sensor system was tested on the thighs of eight volunteer subjects, who walked in a straight forward line in the work space of an optical motion analysis system at three self-selected speeds: slow, normal and fast. In the experiment, the subject was assumed to walk in a straight forward way with very little trunk sway, skin artifacts and no significant internal/external rotation of the leg. The root mean square (RMS) errors of the thigh segment orientation measurement were between 2.4° and 4.9° during normal gait that had a 45° flexion/extension range of motion. Measurement error was observed to increase with increasing walking speed probably because of the result of increased trunk sway, axial rotation and skin artifacts. The results show that, without integration and switching between different sensors, using only one kind of sensor, the wearable sensor system is suitable for ambulatory analysis of normal gait orientation of thigh and shank in two directions of the segment-fixed local coordinate system in 3D space. It can then be applied to assess spatio-temporal gait parameters and monitoring the gait function of patients in clinical settings.     

Impact of limiting visual input on gait: Individuals with Parkinson disease,age-matched controls,and healthy young participants

Normal and limited vision gait was investigated in individuals with Parkinson disease (PD), healthy older and healthy young individuals. Participants walked a GAITRite mat with normal vision or vision of lower limbs occluded. Results indicate individuals with PD walked more slowly, with shorter and wider steps, and spent more time in double support with limited vision as compared to full vision. Healthy young and old individuals took shorter steps but were otherwise unchanged between conditions.     

A kinematic method for computing the motion of the body centre-of-mass (CoM) during walking: a Bayesian approach

The gait pattern of a particular patient can be altered in a large set of pathologies. Tracking the body centre-of-mass (CoM) during the gait allows a quantitative evaluation of these diseases at comparing the gait with normal patterns. A correct estimation of this variable is still an open question because of its non-linearity and inaccurate location. This paper presents a novel strategy for tracking the CoM, using a biomechanical gait model whose parameters are determined by a Bayesian strategy. A particle filter is herein implemented for predicting the model parameters from a set of markers located at the sacral zone. The present approach is compared with other conventional tracking methods and decreases the calculated root mean squared error in about a 56% in the x-axis and 59% in the y-axis.