Automated Classification of Postural Control for Individuals With Parkinson’s Disease Using a Machine Learning Approach: A Preliminary Study

The purposes of the study were (1) to compare postural sway between participants with Parkinson’s disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbor method exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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Automated Classification of Metamodel Repositories: A Machine Learning Approach

2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS) ◽

10.1109/models.2019.00011 ◽

2019 ◽

Cited By ~ 1

Author(s):

Phuong T. Nguyen ◽

Juri Di Rocco ◽

Davide Di Ruscio ◽

Alfonso Pierantonio ◽

Ludovico Iovino

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Automated Classification ◽

Machine Learning Approach

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Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach

Scientific Reports ◽

10.1038/s41598-019-52697-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Ruopeng Sun ◽

Katherine L. Hsieh ◽

Jacob J. Sosnoff

Keyword(s):

Machine Learning ◽

Fall Risk ◽

Postural Sway ◽

Learning Algorithm ◽

Low Risk ◽

Learning Approach ◽

Healthy Controls ◽

Standard Procedures ◽

Machine Learning Approach

Abstract Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n = 34), moderate-risk MS (n = 27), high-risk MS (n = 42). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest – RF, with 10-fold cross validation) to predict individuals’ fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (>86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.

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A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

Journal of Volcanology and Geothermal Research ◽

10.1016/j.jvolgeores.2020.107151 ◽

2020 ◽

pp. 107151

Author(s):

Alexis Falcin ◽

Jean-Philippe Métaxian ◽

Jérôme Mars ◽

Éléonore Stutzmann ◽

Jean-Christophe Komorowski ◽

...

Keyword(s):

Machine Learning ◽

Automatic Classification ◽

Learning Approach ◽

Machine Learning Approach ◽

Volcanic Seismicity

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The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽

10.15282/mekatronika.v2i2.6683 ◽

2020 ◽

Vol 2 (2) ◽

pp. 1-12

Author(s):

Muhammad Nur Aiman Shapiee ◽

Muhammad Ar Rahim Ibrahim ◽

Muhammad Amirul Abdullah ◽

Rabiu Muazu Musa ◽

Noor Azuan Abu Osman ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Nearest Neighbor ◽

Olympic Games ◽

Learning Approach ◽

K Nearest Neighbor ◽

Test Dataset ◽

Machine Learning Approach ◽

Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

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