IMPLEMENTATION OF A SMARTPHONE WIRELESS GYROSCOPE PLATFORM WITH MACHINE LEARNING FOR CLASSIFYING DISPARITY OF A HEMIPLEGIC PATELLAR TENDON REFLEX PAIR

2017 ◽  
Vol 17 (06) ◽  
pp. 1750083 ◽  
Author(s):  
ROBERT LEMOYNE ◽  
TIMOTHY MASTROIANNI
Keyword(s):  
Machine Learning ◽  
Patellar Tendon ◽  
Angular Rate ◽  
Nearest Neighbors ◽  
Reflex Response ◽  
Ordinal Scale ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Tendon Reflex ◽  
Patellar Tendon Reflex

The patellar tendon reflex response provides fundamental means of assessing a subject’s neurological health. Dysfunction regarding the characteristics of the reflex response may warrant the escalation to more advanced diagnostic techniques. Current strategies involve the manual elicitation of the patellar tendon reflex by a highly skilled clinician with subsequent interpretation according to an ordinal scale. The reliability of the ordinal scale approach is a topic of contention. Highly skilled clinicians have been in disagreement regarding even the observation of asymmetric reflex pairs. An alternative strategy incorporated the ubiquitous smartphone with a software application to function as a wireless gyroscope platform for quantifying the reflex response. Each gyroscope signal recording of the reflex response can be conveyed wirelessly through Internet connectivity as an email attachment. The reflex response is evoked through a potential energy impact pendulum that enables prescribed targeting and potential energy level. The smartphone functioning as a wireless gyroscope platform reveals an observationally representative gyroscope signal of the reflex response. Three notably distinguishable attributes of the reflex response are incorporated into a feature set for machine learning: maximum angular rate of rotation, minimum angular rate of rotation, and time disparity between maximum and minimum angular rate of rotation. Four machine learning platforms such as the J48 decision tree, K-nearest neighbors, logistic regression, and support vector machine, were applied to the patellar tendon reflex response feature set incorporating a hemiplegic patellar tendon reflex pair. The J48 decision tree attained 98% classification accuracy, and the K-nearest neighbors, logistic regression, and support vector machine achieved perfect classification accuracy for distinguishing between a hemiplegic affected leg and unaffected leg patellar tendon reflex pair. The research findings reveal the potential of machine learning for enabling advanced diagnostic acuity respective of the gyroscope signal of the patellar tendon reflex response.

scholarly journals Motion Analysis of Normal Patellar Tendon Reflex

2013 ◽  
Vol 40 (6) ◽  
pp. 836-841 ◽  
Author(s):  
Lai Kuan Tham ◽  
Noor Azuan Abu Osman ◽  
Wan Abu Bakar Wan Abas ◽  
Kheng Seang Lim
Keyword(s):  
Motion Analysis ◽  
Patellar Tendon ◽  
Essential Element ◽  
Qualitative Description ◽  
Reflex Response ◽  
Healthy Population ◽  
Reflex Amplitude ◽  
Tendon Reflex ◽  
Patellar Tendon Reflex ◽  
Input Strength

Background:Reflex assessment, an essential element in the investigation of the motor system, is currently assessed through qualitative description, which lacks of normal values in the healthy population. This study quantified the amplitude and latency of patellar tendon reflex in normal subjects using motion analysis to determine the factors affecting the reflex amplitude.Methods:100 healthy volunteers were recruited for patellar tendon reflex assessments which were recorded using a motion analysis system. Different levels of input strength were exerted during the experiments.Results:A linear relationship was found between reflex input and reflex amplitude (r = 0.50, P <0.001). The left knee was found to exhibit 26.3% higher reflex amplitude than the right (P <0.001). The Jendrassik manoeuvre significantly increased reflex amplitude by 34.3% (P = 0.001); the effect was especially prominent in subjects with weak reflex response. Reflex latency normality data were established, which showed a gradual reduction with increasing input strength.Conclusion:The quantitative normality data and findings showed that the present method has great potential to objectively quantify deep tendon reflexes.

scholarly journals Influence of Age on Patellar Tendon Reflex Response

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e80799 ◽  
Author(s):  
Annapoorna Chandrasekhar ◽  
Noor Azuan Abu Osman ◽  
Lai Kuan Tham ◽  
Kheng Seang Lim ◽  
Wan Abu Bakar Wan Abas
Keyword(s):  
Patellar Tendon ◽  
Reflex Response ◽  
Tendon Reflex ◽  
Patellar Tendon Reflex

scholarly journals Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

2021 ◽  
Vol 8 (2) ◽  
pp. 311
Author(s):  
Mohammad Farid Naufal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors

<p class="Abstrak">Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. <em>Computer vision</em> adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan <em>self autonomous machine</em> agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara <em>real time</em>. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari <em>Machine Learning</em> sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

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.

Detection of Loss Zones while Drilling Using Different Machine Learning Techniques

2021 ◽  
pp. 1-29
Author(s):  
Ahmed Alsaihati ◽  
Mahmoud Abughaban ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forests ◽  
Nearest Neighbors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Vector Machines ◽  
Testing Set

Abstract Fluid loss into formations is a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well-control, stuck pipe, and wellbore instability, which, in turn, lead to an increase of well time and cost. This research aims to use and evaluate different machine learning techniques, namely: support vector machines, random forests, and K-nearest neighbors in detecting loss circulation occurrences while drilling using solely drilling surface parameters. Actual field data of seven wells, which had suffered partial or severe loss circulation, were used to build predictive models, while Well-8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Recall, precision, and F1-score measures were used to evaluate the ability of the developed model to detect loss circulation occurrences. The results showed the K-nearest neighbors classifier achieved a high F1-score of 0.912 in detecting loss circulation occurrence in the testing set, while the random forests was the second-best classifier with almost the same F1-score of 0.910. The support vector machines achieved an F1-score of 0.83 in predicting the loss circulation occurrence in the testing set. The K-nearest neighbors outperformed other models in detecting the loss circulation occurrences in Well-8 with an F1-score of 0.80. The main contribution of this research as compared to previous studies is that it identifies losses events based on real-time measurements of the active pit volume.

scholarly journals Machine Learning Approach to Dysphonia Detection

2018 ◽  
Vol 8 (10) ◽  
pp. 1927 ◽  
Author(s):  
Zuzana Dankovičová ◽  
Dávid Sovák ◽  
Peter Drotár ◽  
Liberios Vokorokos
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Speech Features

This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

Conditioned Patellar Tendon Reflex Function in Children with and Without Developmental Coordination Disorders

1995 ◽  
Vol 12 (3) ◽  
pp. 250-261 ◽  
Author(s):  
Harriet G. Williams ◽  
Jeanmarie R. Burke
Keyword(s):  
Patellar Tendon ◽  
Developmental Coordination Disorder ◽  
Reflex Response ◽  
Motoneuron Pool ◽  
Coordination Disorders ◽  
Tendon Reflex ◽  
Patellar Tendon Reflex ◽  
Coordination Disorder ◽  
Developmental Coordination Disorders

A conditioned patellar tendon reflex paradigm was used to study the contributions of crossed spinal and supraspinal inputs to the output of the alpha motoneuron pool in children with and without developmental coordination disorders. The basic patellar tendon reflex response was exaggerated in children with developmental coordination disorders. Crossed spinal and supraspinal influences on the excitability of the alpha motoneuron pool were similar in both groups of children. However, there was evidence of exaggerated crossed spinal and supraspinal inputs onto the alpha motoneuron pool in individual children with developmental coordination disorder.

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