CLASSIFICATION OF SOFTWARE CONTROL ARCHITECTURES FOR A POWERED PROSTHESIS THROUGH CONVENTIONAL GAIT ANALYSIS USING MACHINE LEARNING APPLICATIONS

2019 ◽  
Vol 19 (06) ◽  
pp. 1950044
Author(s):  
ROBERT LEMOYNE ◽  
TIMOTHY MASTROIANNI
Keyword(s):  
Machine Learning ◽  
Gait Analysis ◽  
Plantar Flexion ◽  
Force Plate ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Control Architecture ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Powered Prosthesis

The powered prosthesis for people with transtibial amputation offers the opportunity to more appropriately restore gait functionality with benefits, such as powered plantar flexion. In particular, various software control architectures provide unique capabilities for regulating the powered prosthesis during gait. One highly novel approach applies the winding filament hypothesis, which enables an advanced modeling of muscle characteristics, such as through introducing the attributes of titin into the muscle model. The objective of the research is to contrast the conventional control architecture of the BiOM-powered prosthesis compared with the winding filament hypothesis control architecture through machine learning classification. Four machine learning algorithms are applied through the Waikato Environment for Knowledge Analysis (WEKA): J48 decision tree, [Formula: see text]-nearest neighbors, logistic regression, and the support vector machine. The feature set is derived from the force signal acquired from a force plate, which is a conventional gait analysis system. The feature set applied five attributes representing temporal and kinetic aspects of the stance phase of gait. The [Formula: see text]-nearest neighbors algorithm achieves the best machine learning classification accuracy of 95%. The preliminary research establishes the foundation for more sophisticated endeavors respective of the powered prosthesis, such as determining the appropriateness of modifying the software control architecture to best accommodate the progressive lifestyle evolutions and adaptations of the person with amputation.

scholarly journals Predicting Vasovagal Syncope for Paraplegia Patients Using Average Weighted Ensemble Technique

2021 ◽  
Author(s):  
V. Vinodhini ◽  
Akula Vishalakshi ◽  
G. Naga Chandrika ◽  
S. Sankar ◽  
Somula Ramasubbareddy
Keyword(s):  
Machine Learning ◽  
Vasovagal Syncope ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ensemble Technique ◽  
Machine Learning Classification ◽  
Severe Fatigue ◽  
Artery Disease ◽  
Serious Disease

Vasovagal syncope (VVS) refers to fainting of people with a drop in blood flow to the brain more serious disease in paraplegia patients. Precognitive diagnoses are characterized by lightheadedness, nausea, severe fatigue, and an elevated heart rate. As a result, it’s important to seek care as soon as possible after experiencing syncope. Since receiving a correct diagnosis and appropriate care, the majority of patients may avoid complications with syncope. Syncope appears to be a sign of COVID 19 in people with coronary artery disease. Furthermore, a sudden heart attack might result in acute syncope. In a few circumstances, machine learning classification techniques may not be precise. For paraplegia patients, prediction vasovagal syncope needs more precise results in order to save their lives. The aim of this paper is to use the ensemble technique to improve the accuracy of conventional machine learning algorithms. EEG (ElectroEncephaloGram) brainwave dataset from kaggle is used to implement it. The accuracy of the proposed AWET algorithm is 82%. It improves the accuracy by 17% compare to Support Vector Machine, Random Forest, Naive Bayes, and MultiLayer Perceptron classifiers.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals Three simple steps to improve the interpretability of EEG-SVM studies

2021 ◽  
Author(s):  
Coralie Joucla ◽  
Damien Gabriel ◽  
Emmanuel Haffen ◽  
Juan-Pablo Ortega
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Diagnosis And Prognosis ◽  
Eeg Data ◽  
Clinical Adoption

Research in machine-learning classification of electroencephalography (EEG) data offers important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but the clinical adoption of such systems remains low. We propose here that much of the difficulties translating EEG-machine learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization and cross-validation) and show that, while these 3 aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.

scholarly journals PigLeg: prediction of swine phenotype using machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8764 ◽  
Author(s):  
Siroj Bakoev ◽  
Lyubov Getmantseva ◽  
Maria Kolosova ◽  
Olga Kostyunina ◽  
Duane R. Chartier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Average Daily Gain ◽  
Nearest Neighbors ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Leg Weakness

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

scholarly journals Comparative Analysis of Machine Learning Algorithms with and without Feature Extraction

2020 ◽  
Vol 6 (12) ◽  
pp. 235-239
Author(s):  
Vatsal Gupta and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Image Recognition ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Security Services ◽  
Computational Resources

Image recognition is one of the core disciplines in Computer Vision. It is one of the most widely researched topics of the last few decades. Many advances in image recognition in the past decade, has made it one of the most efficient and powerful disciplines of all, having its applications in every sector including Finance, Healthcare, Security services, Agriculture and many more. Feature extraction is an integral part of image recognition. It helps in training the model more efficiently and with a higher accuracy, by getting rid of any unwanted or unnecessary features, thus reducing the dimensionality of the input image. This also helps in reducing the computational resources required by the algorithm to train, thus making it affordable for people with low end setups. Here we compare the accuracies of different machine learning classification algorithms, and their training times, with and without using feature Extraction. For the purpose of extracting features, a convolutional neural network was used. The model was trained and tested on the data of 12 classes containing a total of 2,175 images. For comparisons, we chose the Logistic regression, K-Nearest Neighbors Classifier, Random forest Classifier, and Support Vector Machine Classifier.

scholarly journals APTITUDE Framework for Learning Data Classification Based on Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Course Content ◽  
Applied Model ◽  
Vector Machines ◽  
Learning Data

Learning analytics refers to the machine learning to provide predictions of learner success and prescriptions to learners and teachers. The main goal of paper is to proposed APTITUDE framework for learning data classification in order to achieve an adaptation and recommendations a course content or flow of course activities. This framework has applied model for student learning prediction based on machine learning. The five machine learning algorithms are used to provide learning data classification: random forest, Naïve Bayes, k-nearest neighbors, logistic regression and support vector machines

scholarly journals Machine Learning Classification Algorithms for Predicting Karenia brevis Blooms on the West Florida Shelf

2021 ◽  
Vol 9 (9) ◽  
pp. 999
Author(s):  
Marvin F. Li ◽  
Patricia M. Glibert ◽  
Vyacheslav Lyubchich
Keyword(s):  
Machine Learning ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Large River ◽  
Karenia Brevis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Economic Damage ◽  
The West ◽  
Machine Learning Classification

Harmful algal blooms (HABs), events that kill fish, impact human health in multiple ways, and contaminate water supplies, have increased in frequency, magnitude, and impacts in numerous marine and freshwaters around the world. Blooms of the toxic dinoflagellate Karenia brevis have resulted in thousands of tons of dead fish, deaths to many other marine organisms, numerous respiratory-related hospitalizations, and tens to hundreds of millions of dollars in economic damage along the West Florida coast in recent years. Four types of machine learning algorithms, Support Vector Machine (SVM), Relevance Vector Machine (RVM), Naïve Bayes classifier (NB), and Artificial Neural Network (ANN), were developed and compared in their ability to predict these blooms. Comparing the 21 year monitoring dataset of K. brevis abundance, RVM and NB were found to have better skills in bloom prediction than the other two approaches. The importance of upwelling-favorable northerly winds in increasing K. brevis probability, and of onshore westerly winds in preventing blooms from dispersing offshore, were quantified using RVM, and all models were used to explore the importance of large river flows and the nutrients they supply in regulating blooms. These models provide new tools for management of these devastating algal blooms.

scholarly journals Impact of Customer Feedback System using Machine Learning Algorithms for Sentiment Mining

2020 ◽  
Vol 9 (4) ◽  
pp. 1475-1483
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Product Reviews ◽  
Machine Learning Classification ◽  
Customer Feedback ◽  
Specificity And Sensitivity ◽  
Sentiment Mining

The process of discovering and analyzing the customer feedback using Natural Language Processing (NLP) is said to be sentiment analysis. Based on the surge over the concept of rating level in sentiment analysis, sentiment is utilized as an attribute for certain aspects or features that get expressed and more attention are provided to the problem of detecting the customer reviews. Despite the wide use and popularity of some methods, a better technique for identifying the polarity of a text data is hard to find. Machine learning has recently attracted attention as an approach for sentiment analysis. This work extends the idea of evaluating the performance of various Machine Learning (ML) classifiers namely logistic regression, Naive Bayes, Support Vector Machine (SVM) and Neural Network (NN).To show their effectiveness in sentiment mining of customer product reviews, the customer feedback has been collected from Grocery and Gourmet Food. Nearly 90 thousands customers feedback reviews of various product related categories namely Product ID, rating, review test, review time reviewer ID and reviewer name are used in this analysis. The performance of the classifiers is measured in terms of accuracy, specificity and sensitivity. From the experimental results, the better machine learning classification algorithm is proposed for sentiment mining using online shopping customer review data.

scholarly journals Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors

2021 ◽  
Vol 2095 (1) ◽  
pp. 012058
Author(s):  
Xiaoyu Xian ◽  
Haichuan Tang ◽  
Yin Tian ◽  
Qi Liu ◽  
Yuming Fan
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Fault Type ◽  
Traction Motors ◽  
Forest Classification

Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

An analysis of PCOS disease prediction model using machine learning classification algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Shivani Aggarwal ◽  
Kavita Pandey
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Metabolic Abnormalities ◽  
Related Disorder ◽  
Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

Sign in / Sign up

Export Citation Format

Share Document