scholarly journals Individuals have unique muscle activation signatures as revealed during gait and pedaling

2019 ◽  
Vol 127 (4) ◽  
pp. 1165-1174 ◽  
Author(s):  
François Hug ◽  
Clément Vogel ◽  
Kylie Tucker ◽  
Sylvain Dorel ◽  
Thibault Deschamps ◽  
...  
Keyword(s):  
Machine Learning ◽  
Muscle Activation ◽  
Vastus Lateralis ◽  
Support Vector ◽  
Experimental Session ◽  
Learning Approach ◽  
Classification Rate ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Machine Learning Approach

Although it is known that the muscle activation patterns used to produce even simple movements can vary between individuals, these differences have not been considered to prove the existence of individual muscle activation strategies (or signatures). We used a machine learning approach (support vector machine) to test the hypothesis that each individual has unique muscle activation signatures. Eighty participants performed a series of pedaling and gait tasks, and 53 of these participants performed a second experimental session on a subsequent day. Myoelectrical activity was measured from eight muscles: vastus lateralis and medialis, rectus femoris, gastrocnemius lateralis and medialis, soleus, tibialis anterior, and biceps femoris -long head. The classification task involved separating data into training and testing sets. For the within-day classification, each pedaling/gait cycle was tested using the classifier, which had been trained on the remaining cycles. For the between-day classification, each cycle from day 2 was tested using the classifier, which had been trained on the cycles from day 1. When considering all eight muscles, the activation profiles were assigned to the corresponding individuals with a classification rate of up to 99.28% (2,353/2,370 cycles) and 91.22% (1,341/1,470 cycles) for the within-day and between-day classification, respectively. When considering the within-day classification, a combination of two muscles was sufficient to obtain a classification rate >80% for both pedaling and gait. When considering between-day classification, a combination of four to five muscles was sufficient to obtain a classification rate >80% for pedaling and gait. These results demonstrate that strategies not only vary between individuals, as is often assumed, but are unique to each individual. NEW & NOTEWORTHY We used a machine learning approach to test the uniqueness and robustness of muscle activation patterns. We considered that, if an algorithm can accurately identify participants, one can conclude that these participants exhibit discernible differences and thus have unique muscle activation signatures. Our results show that activation patterns not only vary between individuals, but are unique to each individual. Individual differences should, therefore, be considered relevant information for addressing fundamental questions about the control of movement.

scholarly journals Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2328 ◽  
Author(s):  
Md Shafiullah ◽  
M. Abido ◽  
Taher Abdel-Fattah
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Approach ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Performance Indices ◽  
Machine Learning Approach ◽  
Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

scholarly journals Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

2019 ◽  
pp. 1-12
Author(s):  
Mokhtar Al-Suhaiqi ◽  
Muneer A. S. Hazaa ◽  
Mohammed Albared
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approach ◽  
Plagiarism Detection ◽  
Machine Learning Approach ◽  
Cross Lingual ◽  
Cross Language

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity  and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM   classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used. 

scholarly journals Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

2020 ◽  
Vol 10 (16) ◽  
pp. 5673 ◽  
Author(s):  
Daniela Cardone ◽  
David Perpetuini ◽  
Chiara Filippini ◽  
Edoardo Spadolini ◽  
Lorenza Mancini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress State ◽  
Thermal Imaging ◽  
Driving Simulator ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Thermal Features ◽  
Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

Squat Muscle Activation Patterns with Hip Rotations in Subjects with Genu Varum Deformity

2020 ◽  
Author(s):  
Mohamadreza Hatefi ◽  
Farideh Babakhani ◽  
Ramin Balouchi ◽  
Amir Letafatkar ◽  
Brian J. Wallace
Keyword(s):  
Muscle Activation ◽  
Activity Ratio ◽  
External Rotation ◽  
Vastus Lateralis ◽  
Fascia Lata ◽  
Genu Varum ◽  
Vastus Medialis ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Hip Rotation

AbstractThe purpose of this study was to compare muscle activation during the squat with different hip rotations (neutral, 15, 30, and 45° of internal and external hip rotation) in subjects with and without Genu Varum deformity deformity. Surface electromyography were recorded from 32 men with (n=16) and without (n=16) Genu Varum deformity. In the Genu Varum deformity group, the squats with 30, 45 and 15° of internal rotations of the hip showed significantly greater gluteus medius activation as compared to other positions. Moreover, the tensor fascia lata activity increased with greater external rotation of the hip, and significantly more than hip internal rotations (p<0.05). For vastus medialis and vastus lateralis, both hip internal and external rotation showed a significantly greater activation compared to the neutral hip positions (p<0.05). There were significant differences in the gluteus medius:tensor fascia lata activity ratio (p=0.001) and the vastus medialis: vastus lateralis activity ratio (p=0.001) between the different hip positions in the Genu Varum deformity and healthy groups. These results demonstrate that muscle activation patterns varied significantly with the position of different hip rotation in both groups. Those with Genu Varum deformity may use this information to aid in an injury prevention strategy by choosing squat positioning that favorably alters muscle activation patterns.

scholarly journals Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach

Cancers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 431 ◽  
Author(s):  
Oneeb Rehman ◽  
Hanqi Zhuang ◽  
Ali Muhamed Ali ◽  
Ali Ibrahim ◽  
Zhongwei Li
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Information Gain ◽  
Support Vector ◽  
Learning Approach ◽  
Breast Cancers ◽  
Functional Studies ◽  
Normal Tissues ◽  
Machine Learning Approach ◽  
Chi Squared

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

scholarly journals Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis

Life ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 181
Author(s):  
Christopher T. Mandrell ◽  
Torrey E. Holland ◽  
James F. Wheeler ◽  
Sakineh M. A. Esmaeili ◽  
Kshitij Amar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Raman Spectra ◽  
Pancreatic Cancer Cell Line ◽  
Cancer Cell Line ◽  
Human Pancreatic Cancer ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.

scholarly journals Effects of Short-Term Training With Uncoupled Cranks in Trained Cyclists

2012 ◽  
Vol 7 (2) ◽  
pp. 113-120 ◽  
Author(s):  
Jack M. Burns ◽  
Jeremiah J. Peiffer ◽  
Chris R. Abbiss ◽  
Greig Watson ◽  
Angus Burnett ◽  
...  
Keyword(s):  
Muscle Activation ◽  
Vastus Lateralis ◽  
Biceps Femoris ◽  
Training Group ◽  
Recovery Phase ◽  
Muscle Recruitment ◽  
Short Term ◽  
Activation Patterns ◽  
Gross Efficiency ◽  
Muscle Activation Patterns

Purpose:Manufacturers of uncoupled cycling cranks claim that their use will increase economy of motion and gross efficiency. Purportedly, this occurs by altering the muscle-recruitment patterns contributing to the resistive forces occurring during the recovery phase of the pedal stroke. Uncoupled cranks use an independent-clutch design by which each leg cycles independently of the other (ie, the cranks are not fixed together). However, research examining the efficacy of training with uncoupled cranks is equivocal. The purpose of this study was to determine the effect of short-term training with uncoupled cranks on the performance-related variables economy of motion, gross efficiency, maximal oxygen uptake (VO2max), and muscle-activation patterns.Methods:Sixteen trained cyclists were matched-paired into either an uncoupled-crank or a normal-crank training group. Both groups performed 5 wk of training on their assigned cranks. Before and after training, participants completed a graded exercise test using normal cranks. Expired gases were collected to determine economy of motion, gross efficiency, and VO2max, while integrated electromyography (iEMG) was used to examine muscle-activation patterns of the vastus lateralis, biceps femoris, and gastrocnemius.Results:No significant changes between groups were observed for economy of motion, gross efficiency, VO2max, or iEMG in the uncoupled- or normal-crank group.Conclusions:Five weeks of training with uncoupled cycling cranks had no effect on economy of motion, gross efficiency, muscle recruitment, or VO2max compared with training on normal cranks.

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