Human knee abnormality detection from imbalanced sEMG data

Vijayvargiya, Ankit; Prakash, Chandra; Kumar, Rajesh; Bansal, Sanjeev K.; Tavares, João Manuel R. S.

doi:10.1016/j.bspc.2021.102406

Cited by 43 publications

(21 citation statements)

References 49 publications

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“…Recently comparative analysis approaches in classifying imbalanced and balanced datasets are widespread in the literature. The study by Vijayvargiya et al [ 38 ] was used various machine learning models on the original normal and abnormal subjects about knee from electromyography (EMG) data. The extra tree classifier found the best accuracy after oversampling at 93.3%.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan

Rahim

Salim

et al. 2022

Journal of Healthcare Engineering

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In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan

Rahim

Salim

et al. 2022

Journal of Healthcare Engineering

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“…The UCIEMG dataset in Lower Limb Data Set collected by Sanchez et al [28] to address the pathological abnormalities [29] is used in this study. It includes data from 22 male subjects performing three different exercises, 11 of which have some knee abnormality.…”

Section: A Datasetmentioning

confidence: 99%

Human Activity Recognition from Knee Angle Using Machine Learning Techniques

Nazari

Nahavandi

Mohajer

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Human Activity Recognition (HAR) is a crucial technology for many applications such as smart homes, surveillance, human assistance and health care. This technology utilises pattern recognition and can contribute to the development of human-in-the-loop control of different systems such as orthoses and exoskeletons. The majority of reported studies use a small dataset collected from an experiment for a specific purpose. The downsides of this approach include: 1) it is hard to generalise the outcome to different people with different biomechanical characteristics and health conditions, and 2) it cannot be implemented in applications other than the original experiment. To address these deficiencies, the current study investigates using a publicly available dataset collected for pathology diagnosis purposes to train Machine Learning (ML) algorithms. A dataset containing knee motion of participants performing different exercises has been used to classify human activity. The algorithms used in this study are Gaussian Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors Vote, Support Vector Machine and Gradient Boosting. Furthermore, two training approaches are compared to raw data (de-noised) and manually extracted features. The results show up to 0.94 performance of the Area Under the ROC Curve (AUC) metric for 11-fold cross-validation for Gradient Boosting algorithm using raw data. This outcome reflects the validity and potential use of the proposed approach for this type of dataset.

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“…Imbalanced data is a common phenomenon in the development of machine learning models, not only in geoscienti c problems but in almost all arti cial intelligence problems, such as medical diagnosis (e.g., Vijayvargiya et al, 2021). Imbalanced data considerably reduces a model's capacity to perform predictions, especially for the minority class, where the recognition rate decreases considerably (Japkowicz and Stephen, 2002).…”

Section: Data Balancingmentioning

confidence: 99%

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

Silva

Ferreira

Costa

et al. 2021

Preprint

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Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

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Human knee abnormality detection from imbalanced sEMG data

Cited by 43 publications

References 49 publications

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Human Activity Recognition from Knee Angle Using Machine Learning Techniques

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

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