Autism Spectrum Disorder and Normal Gait Classification Using Machine Learning Approach

Previous research has reported that children with autism spectrum disorder (ASD) exhibit unusual movement and atypical gait patterns. Automated classification of abnormal gait from normal gait can serve as a potential tool for early and objective diagnosis as well as post-treatment monitoring. The aim of this study is to employ machine learning approaches to differentiate between children with ASD and healthy controls by utilizing gait features extracted from three-dimensional (3D) gait analysis data. The gait data of 30 children with ASD and 30 healthy controls were obtained using 3D gait analysis during walking at a normal pace. Time-series parameterization techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Further, the dominant gait features were selected using statistical feature selection techniques. To highlight the efficacy of different machine learning classifiers towards devising an accurate gait classification, four machine learning classifiers were trained to classify ASD and control gait based on the selected dominant gait features. The classifiers are Artificial Neural Networks (ANN), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The 10-fold cross-validation test results indicate that the ANN-SCG classifier with six dominant gait features was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100% specificity. The findings indicate that the ANN classifier has the potential to serve as a valuable tool for assisting in the diagnosis of ASD gait and evaluating treatment programs

The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

A review of the effectiveness of lower limb orthoses used in cerebral palsy

To produce this review, a systematic literature search was conducted for relevant articles published in the period between the date of the previous ISPO consensus conference report on cerebral palsy (1994) and April 2008. The search terms were 'cerebral and pals* (palsy, palsies), 'hemiplegia', 'diplegia', 'orthos*' (orthoses, orthosis) orthot* (orthotic, orthotics), brace or AFO

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

The test-retest reliability of centre of pressure measures in bipedal static task conditions - A systematic review of the literature

Summary of background data: The analysis of centre of pressure (COP) excursions is used as an index of postural stability in standing. Conflicting data have been reported over the past 20 years regarding the reliability of COP measures and no standard procedure for COP measure use in study design has been established. Search methods: Six online databases (January 1980 to February 2009) were systematically searched followed by a manual search of retrieved papers. Results: Thirty-two papers met the inclusion criteria. The majority of the papers (26/32, 81.3%) demonstrated acceptable reliability. While COP mean velocity (mVel) demonstrated variable but generally good reliability throughout the different studies (r= 0.32-0.94), no single measurement of COP appeared significantly more reliable than the others. Regarding data acquisition duration, a minimum of 90 s is required to reach acceptable reliability for most COP parameters. This review further suggests that while eyes closed readings may show slightly higher reliability coefficients, both eyes open and closed setups allow acceptable readings under the described conditions (r≥0.75). Also averaging the results of three to five repetitions on firm surface is necessary to obtain acceptable reliability. A sampling frequency of 100. Hz with a cut-off frequency of 10. Hz is also recommended. No final conclusion regarding the feet position could be reached. Conclusions: The studies reviewed show that bipedal static COP measures may be used as a reliable tool for investigating general postural stability and balance performance under specific conditions. Recommendations for maximizing the reliability of COP data are provided

Extraction of Stride Events From Gait Accelerometry During Treadmill Walking

Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial accelerometer accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings