SVM based machine learning approach to identify Parkinson's disease using gait analysis

Shetty, Sachin; Rao, Yunbo

doi:10.1109/inventive.2016.7824836

Cited by 74 publications

(37 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, there have been many attempts to classify and distinguish abnormal pathologic gait patterns via combining machine learning-based techniques with gait analysis system in the medical field [15][16][17][18]. When analyzing gait patterns through machine learning-based techniques, a machine classifier is necessary and the support vector machine (SVM) has been established as a successful technique for pattern recognition [19].…”

Section: Introductionmentioning

confidence: 99%

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

et al. 2020

View full text Add to dashboard Cite

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

et al. 2020

View full text Add to dashboard Cite

show abstract

“…An improved covariance gait evaluation method and a K-NN classifier are used to distinguish between normal and abnormal gait models, and the results are compared on available pathological gait data sets. In [12], the author extracts the best seven feature vectors for Parkinson's disease (PD), and then classifies them using a support vector machine (SVM) classifier based on the Gauss radial basis function kernel, which can effectively distinguish Parkinson's disease from other nervous system diseases. A new end-to-end method [8] based on in-depth learning is proposed.…”

Section: Related Workmentioning

confidence: 99%

Abnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network

Gao

Ren

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This paper presents a novel approach to human gait analysis with a sensor-based technique involving a wearable inertial measurement unit (IMU). The proposed system emphasizes the detection of certain abnormal gait patterns, including hemiplegic, tiptoe, and cross-threshold gait. First, we use the dynamic step conjugate gradient algorithm to calculate the attitude of the gait data, and we then use the gait feature information location algorithm to segment the attitude data. The segmented attitude data are used as input in the classification model. In this paper, we propose an algorithm based on a long short-term memory network and convolutional neural network (LCWSnet) for diagnosis and classification of abnormal gait patterns using the leg Euler angle information, and parameters related to features can be adjusted adaptively according to the feedback of objectives and optimization functions. We optimize the convergence layer of the LSTM-CNN model and improve the classification accuracy of abnormal gait. The experimental results demonstrate that the proposed LCWSnet-based technique is able to detect gait abnormality in the data. The proposed personalized gait classification approach is accurate and reliable and can be implemented for the abnormal gait. INDEX TERMS Convolutional neural network, dynamic step conjugate gradient algorithm, gait feature information location, long short-term memory network, wireless body area network.

show abstract

“…The utilisation of machine learning paradigms for automating classification problems has become common practice [12], [13]. Support Vector Machines (SVM) and K-Nearest Neighbours (KNN) are both commonly applied models when dealing with binary classification problems [14], [15], and for this reason, they act as good baseline classifiers for comparison and analysis of newly proposed models.…”

Section: Introductionmentioning

confidence: 99%

Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

Henderson

Yogarajah

Gardiner

et al. 2020

2020 31st Irish Signals and Systems Conference (ISSC)

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a developmental disorder that is prevalent globally. Research into detecting autism traditionally focused on behavioural aspects of the condition, however, more recently, focus has shifted to more objective alternatives using techniques such as machine learning and gait analysis. Gait measurements, having been used for person identification, varies from person to person, introducing a lot of intra-subject variance. This applies to the 8 spatialtemporal features used in this study, representing the time that an individual spends in each phase of a gait cycle, collected using a Vicon motion tracking system. The features were averaged across each gait trial that the subjects performed, producing a second set of features with reduced intra-subject variance. Four common classifiers, a Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forests (RF) and a Decision Tree (DT) classifier, were all trained using the two feature sets and their classification rates were compared. The results show that for the RF classifier, reducing the intra-subject variance, was able to successfully increase the classification power. The KNN and DT classifiers experienced a minimal decrease in accuracy, where the SVM suffered the greatest loss when intrasubject variance was reduced. Results overall show that the effect intra-subject variance has on classification power depends heavily on the suitability of the classifier to the initial problem as well as size and class balance of the data.

show abstract

SVM based machine learning approach to identify Parkinson's disease using gait analysis

Cited by 74 publications

References 8 publications

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

Abnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network

Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

Contact Info

Product

Resources

About