Potential of the genetic algorithm neural network in the assessment of gait patterns in ankle arthrodesis

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

An automated gait classification method is developed in this study, which can be applied to analysis and to classify pathological gait patterns using 3D ground reaction force (GRFs) data. The study involved the discrimination of gait patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The acquired 3D GRFs data were categorized into three groups. Two different algorithms were used to extract the gait features; the GRFs parameters and the discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC) and artificial neural networks (ANN) were also investigated for the classification of gait features in this study. Furthermore, different feature sets were formed using a combination of the 3D GRFs components (mediolateral, anterioposterior, and vertical) and their various impacts on the acquired results were evaluated. The best leave-one-out (LOO) classification accuracy 85% was achieved. The results showed some improvement through the application of a features selection algorithm based on M-shaped value of vertical force and the statistical test ANOVA of mediolateral and anterioposterior forces. The optimal feature set of six features enhanced the accuracy to 95%. This work can provide an automated gait classification tool that may be useful to the clinician in the diagnosis and identification of pathological gait impairments.

Probabilistic gait classification in children with cerebral palsy: a Bayesian approach

Three-dimensional gait analysis (3DGA) generates a wealth of highly variable data. Gait classifications help to reduce, simplify and interpret this vast amount of 3DGA data and thereby assist and facilitate clinical decision making in the treatment of CP. CP gait is often a mix of several clinically accepted distinct gait patterns. Therefore, there is a need for a classification which characterizes each CP gait by different degrees of membership for several gait patterns, which are considered by clinical experts to be highly relevant. In this respect, this paper introduces Bayesian networks (BN) as a new approach for classification of 3DGA data of the ankle and knee in children with CP. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. Furthermore, they provide an explicit way of introducing clinical expertise as prior knowledge to guide the BN in its analysis of the data and the underlying clinically relevant relationships. BNs also enable to classify gait on a continuum of patterns, as their outcome consists of a set of probabilistic membership values for different clinically accepted patterns. A group of 139 patients with CP was recruited and divided into a training- (n=80% of all patients) and a validation-dataset (n=20% of all patients). An average classification accuracy of 88.4% was reached. The BN of this study achieved promising accuracy rates and was found to be successful for classifying ankle and knee joint motion on a continuum of different clinically relevant gait patterns.

Neural network estimation of balance control during locomotion

Gait patterns of the elderly are often adjusted to accommodate for reduced function in the balance control system and a general reduction in skeletal muscle strength. Recent studies have demonstrated that measures related to motion of whole body center of mass (COM) can distinguish elderly individuals with balance impairment from healthy peers. Accurate COM estimation requires a multiple-segment anthropometric model, which may restrict its broad application in assessment of dynamic instability. Although temporal-distance measures and electromyography have been used in evaluation of overall gait function and determination of gait dysfunction, no studies have examined the use of gait measurements in predicting COM motion during gait. The purpose of this study was to demonstrate the effectiveness of an artificial neural network (ANN) model in mapping gait measurements onto COM motion in the frontal plane. Data from 40 subjects of varied age and balance impairment were entered into a 3-layer feed-forward model with back-propagated error correction. Bootstrap re-sampling was used to enhance the generalization accuracy of the model, using 20 re-sampling trials. The ANN model required minimal processing time (5 epochs, with 20 hidden units) and accurately mapped COM motion (R-values up to 0.89). As training proportion and number of hidden units increased, so did model accuracy. Overall, this model appears to be effective as a mapping tool for estimating balance control during locomotion. With easily obtained gait measures as input and a simple, computationally efficient architecture, the model may prove useful in clinical scenarios where electromyography equipment exists.

Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems

The technology supporting the analysis of human motion has advanced dramatically. Past decades of locomotion research have provided us with significant knowledge about the accuracy of tests performed, the understanding of the process of human locomotion, and how clinical testing can be used to evaluate medical disorders and affect their treatment. Gait analysis is now recognized as clinically useful and financially reimbursable for some medical conditions. Yet, the routine clinical use of gait analysis has seen very limited growth. The issue of its clinical value is related to many factors, including the applicability of existing technology to addressing clinical problems; the limited use of such tests to address a wide variety of medical disorders; the manner in which gait laboratories are organized, tests are performed, and reports generated; and the clinical understanding and expectations of laboratory results. Clinical use is most hampered by the length of time and costs required for performing a study and interpreting it. A "gait" report is lengthy, its data are not well understood, and it includes a clinical interpretation, all of which do not occur with other clinical tests. Current biotechnology research is seeking to address these problems by creating techniques to capture data rapidly, accurately, and efficiently, and to interpret such data by an assortment of modeling, statistical, wave interpretation, and artificial intelligence methodologies. The success of such efforts rests on both our technical abilities and communication between engineers and clinicians.

On the Robustness of Stride Frequency Estimation

The robustness of stride frequency estimation (location and spread) from stride period data is investigated using influence functions. Theoretical analysis reveals that stride frequency estimates by Stokes et al. and by direct calculation have unbounded influence functions and zero breakdown points, implying a lack of both local and global robustness. Comparison of estimates obtained from an ensemble of pathological gait stride time series shows that on average, differences among estimators are not statistically significant (p > or = 0.59) for long time series (hundreds of strides). Specific circumstances under which nonrobust estimates depart from robust estimates are investigated in terms of outlier influence. We recommend some heuristic rules-of-thumb for prudent selection of nonrobust stride frequency estimators for a given stride time series. The theoretical and empirical estimator comparisons suggest that in general, more research on estimator robustness in quantitative gait analysis is warranted.

Relationship between pain and vertebral motion in chronic low-back pain subjects

OBJECTIVES

To investigate the relationship between intervertebral motion, intravertebral deformation and pain in chronic low-back pain patients.

DESIGN

This study measured vertebral motion of the lumbar spine and associated pain in a select group of chronic low-back pain patients as they performed a standard battery of motions in all planes.

BACKGROUND

Numerous studies have demonstrated that individuals with low-back pain have impaired spinal motion, yet few studies have examined the specific relationship between pain and motion parameters. Although it is accepted that the pain in mechanical low-back patients is due to specific spinal motions, no studies have related specific motions to pain measures.

METHODS

Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. The external fixator frame was removed following the clinical external fixation test. The 3D locations of the pedicle screws and the level of pain were recorded as the subjects performed a battery of motions. The relationship between the pain and motion parameters was assessed using linear discriminant analysis and neural network models.

RESULTS

The neural network model showed a strong relationship between observed and predicted pain (R(2)=0.997). The discriminant analysis showed a weak relationship (R(2)=0.5).

CONCLUSIONS

Vertebral motion parameters are strongly predictive of pain in this select group of chronic low-back pain patients. The nature of the relationship is nonlinear and involves interactions; neural networks are able to effectively describe these relationships.

RELEVANCE

Specific patterns of intervertebral motion and intravertebral deformation result in pain in chronic low-back pain patients. This substantiates the mechanical back pain aetiology.