Revealing the unique features of each individual's muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

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Revealing the unique features of each individual’s muscle activation signatures

10.1101/2020.07.23.217034 ◽

2020 ◽

Author(s):

Jeroen Aeles ◽

Fabian Horst ◽

Sebastian Lapuschkin ◽

Lilian Lacourpaille ◽

François Hug

Keyword(s):

Muscle Activation ◽

Conclusive Evidence ◽

Classification Model ◽

Support Vector ◽

Machine Learning Classification ◽

Individual Muscle ◽

Lower Limb Muscles ◽

Model Predictions ◽

Exact Origin ◽

Individual Input

AbstractThere is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualising each individual’s muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

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Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

Malaysian Journal of Applied Sciences ◽

10.37231/myjas.2021.6.1.276 ◽

2021 ◽

Vol 6 (1) ◽

pp. 67-79

Author(s):

Olalekan Awujoola ◽

Philip O Odion ◽

Martins E Irhebhude ◽

Halima Aminu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Job Applicants ◽

Classification Model ◽

Management Decision ◽

Support Vector ◽

Tertiary Institution ◽

Machine Learning Classification ◽

Higher Institution ◽

Management Decision Making

Several higher institution of learning faces issue or difficulty of turning out more than 90% of their graduates who can competently satisfy and meet the requirements of the industry. However, the industry is also confronted with the difficulty of sourcing skilled tertiary institution graduates that match their needs. Failure or success of any organization depends mostly on how its workforce is recruited and retained. Therefore, the selection of an acceptable or satisfactory candidate for the job position is one of the major and vital problems of management decision-making. This work, therefore, proposes a modern, accurate and worthy machine learning classification model that can be deployed, implemented, and put to use when making predictions and assessments on job applicant's attributes from their academic performance datasets in other to meet the selection criteria for the industry. Both supervised and unsupervised machine learning classifiers were considered in this work. Naïve Bayes, Logistic Regression, support vector machine (SVM). Random Forest and Decision tree performed well, but Logistic Regression outperformed others with 93% accuracy.

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Comparison of Machine-Learning Classification Models for Glaucoma Management

Journal of Healthcare Engineering ◽

10.1155/2018/6874765 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 11

Author(s):

Guangzhou An ◽

Kazuko Omodaka ◽

Satoru Tsuda ◽

Yukihiro Shiga ◽

Naoko Takada ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Laser Speckle ◽

Classification Model ◽

Background Information ◽

Laser Speckle Flowgraphy ◽

Support Vector ◽

Classification Models ◽

Machine Learning Classification ◽

Optic Discs

This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients’ background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.

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Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

Water ◽

10.3390/w13172387 ◽

2021 ◽

Vol 13 (17) ◽

pp. 2387

Author(s):

Fernando Salazar ◽

André Conde ◽

Joaquín Irazábal ◽

David J. Vicente

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Anomaly Detection ◽

Classification Model ◽

Misclassification Rate ◽

Joint Analysis ◽

Support Vector ◽

Machine Learning Classification ◽

Vector Machines ◽

One Class Classification

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

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Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years

Medicina ◽

10.3390/medicina57111230 ◽

2021 ◽

Vol 57 (11) ◽

pp. 1230

Author(s):

Jae-Geum Shim ◽

Kyoung-Ho Ryu ◽

Eun-Ah Cho ◽

Jin Hee Ahn ◽

Hong Kyoon Kim ◽

...

Keyword(s):

Machine Learning ◽

Back Pain ◽

Lower Back Pain ◽

Machine Learning Algorithms ◽

Classification Model ◽

Gradient Boosting ◽

Support Vector ◽

Chronic Lower Back Pain ◽

Machine Learning Classification ◽

Lower Back

Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP.

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Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v4i1.3798 ◽

2020 ◽

Vol 4 (1) ◽

pp. 86-96

Author(s):

Ricky Risnantoyo ◽

Arifin Nugroho ◽

Kresna Mandara

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Learning Algorithm ◽

Classification Model ◽

Support Vector ◽

K Nearest Neighbor ◽

The Public ◽

Machine Learning Classification ◽

Corona Virus

Corona virus outbreaks that occur in almost all countries in the world have an impact not only in the health sector, but also in other sectors such as tourism, finance, transportation, etc. This raises a variety of sentiments from the public with the emergence of corona virus as a trending topic on Twitter social media. Twitter was chosen by the public because it can disseminate information in real time and can see market reactions quickly. This research uses "tweet" data or public tweet related to "Corona Virus" to see how the sentiment polarity arises. Text mining techniques and three machine learning classification algorithms are used, including Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) to build a tweet classification model of sentiments whether they have positive, negative, or neutral polarity. The highest test results are generated by the Support Vector Machine (SVM) algorithm with an accuracy value of 76.21%, a precision value of 78.04%, and a recall value of 71.42%.Keywords: Machine Learning, Corona Virus, Twitter, Sentiment Analysis.

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Prediction of Spam Email using Machine Learning Classification Algorithm

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35226 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1107-1112

Author(s):

P Sai Teja

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Classification Model ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Adaptive Boosting ◽

Machine Learning Classification ◽

E Mail

Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.

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A Method for the Definition of Immunological Non-response to Antiretroviral Therapy Based on Review Analysis and Supervised Classification Model

10.21203/rs.3.rs-1090137/v1 ◽

2021 ◽

Author(s):

Yong Shuai

Keyword(s):

Supervised Learning ◽

Supervised Classification ◽

Absolute Number ◽

Learning Model ◽

Classification Model ◽

Support Vector ◽

Infected People ◽

Machine Learning Classification ◽

Visualization Technology ◽

Definition Of

Abstract BackgroundImmunological non-response (INR) accelerated the progression of AIDS disease and brought serious difficulties to the treatment of HIV-1 infected people. The current definition of INR lacked a credible consensus, which affected the diagnosis, treatment and scientific research of INR. MethodWe systematically analyzed the open source INR related references, used visualization techniques and machine learning classification models to propose the features, models and criteria that define INR. ResultWe summarized some consensus on the definition of INR. Among the features that defined INR, CD4+ T-cell absolute number and ART time were the best feature to define INR . The supervised learning classification model had high accuracy in defining INR, and the support vector machine (SVM) had the highest accuracy in the commonly used supervised classification learning model. Based on supervised learning model and visualization technology, we proposed some criteria that could help to reach a consensus on INR definition. ConclusionsThis study provided consensus, features, model and criteria for defining INR.

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An Optimized E-Lecture Video Retrieval based on Machine Learning Classification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9114.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4820-4827

Keyword(s):

Machine Learning ◽

Text Classification ◽

Naive Bayes ◽

Video Retrieval ◽

Naïve Bayes ◽

Classification Algorithm ◽

Classification Model ◽

Support Vector ◽

Machine Learning Classification ◽

E Learning

The advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better performance both in terms of time and also with respect to relevancy of the search results

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