scholarly journals A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification

2019 ◽  
Vol 9 (9) ◽  
pp. 1741 ◽  
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Neila Mezghani
Keyword(s):  
Three Dimensional ◽  
Data Representation ◽  
Small Sample ◽  
Repeated Measurements ◽  
Training Data ◽  
Multidimensional Data ◽  
Knee Kinematic ◽  
Kinematic Data ◽  
Pathology Classification ◽  
Knee Pathology

Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods.

scholarly journals Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals

2019 ◽  
Vol 1 (3) ◽  
pp. 768-784
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Youssef Ouakrim ◽  
Neila Mezghani
Keyword(s):  
Knee Osteoarthritis ◽  
Reference Data ◽  
Small Sample ◽  
Repeated Measurements ◽  
Kinematic Data ◽  
High Data ◽  
Current State ◽  
Pathology Classification ◽  
Discrete Curves ◽  
Knee Pathology

The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling T 2 statistic. This classifier uses the whole sample of an individual’s measurements for a better support to classification, and the Hotelling T 2 hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of 88.1 % , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications.

scholarly journals Knee kinematic curve representation and application to knee pathology classification

2018 ◽  
Vol 4 (1) ◽  
pp. 32 ◽  
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Youssef Ouakrim ◽  
Neila Mezghani
Keyword(s):  
Variational Method ◽  
Support Vector ◽  
Single Individual ◽  
Knee Kinematic ◽  
Classification Problems ◽  
Essential Information ◽  
Pathology Classification ◽  
Curve Representation ◽  
Knee Pathology ◽  
Corrected Curve

This study investigates a variational method to determine the most representative shape of a set of knee kinematic curves with application to knee pathology classification. Although they provide essential information for pathology classification, knee kinematic curves are characterized by high intra-class variability and outliers are often present. As a result, a set of several measurement curves are acquired of any single individual which are then averaged before their use for pathology classification. Rather than using the average of an individual’s recorded measurement curves, this method determines a better representative curve by first correcting the data to account for outliers occurrence and class variability using a variational method. The correction is performed by simultaneous minimization of a set of objective functions, one for each curve in the measurement set, and consisting of a weighed sum of two terms: a data term of conformity of the corrected curve to the given curve, and a regularization term of proximity of the corrected curve to the mean of all the corrected curves to inhibit the influence of outliers in the set. Validation tests were performed to discriminate between knee osteoarthritis data (OA) and non-OA data. Using a support vector machine, the classification accuracy with the proposed representation was 86%, with 81% sensitivity and 90% specificity, compared to 83% accuracy for the standard representation by average, with 76% sensitivity and 90% specificity. The representation has also been tested within the OA category to distinguish the femero-tibial patholgy from the femero-patellar, giving 76% accuracy, with 76% sensitivity and 76% specificity, compared to 69% accuracy, with 62% sensitivity and 76% specificity. These significant improvements by the proposed method warrant its further investigation by application to other biomedical engineering pattern classification problems and datasets.

scholarly journals COORDINATE SYSTEM FOR REPRESENTATION OF MULTIDIMENSIONAL DATA

2018 ◽  
pp. 40-44
Author(s):  
A. B. Lachikhina ◽  
K. N. Soldatov
Keyword(s):  
Three Dimensional ◽  
Data Representation ◽  
Three Dimensions ◽  
Multidimensional Data ◽  
Independent Data ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Proposed Model ◽  
Multidimensional Array ◽  
Number Of Faces

Visualization of analyzing multidimensional data is often required in order to improve perception and visibility. The purpose of this research is a multidimensional array of data representation. It is proposed to use a three-dimensional model as a tool. The methods used to represent an array of data with more than three dimensions are presented. The principle of constructing a multidimensional array cell is considered. An example of the constructed hypercube cell is given. The formulas for calculating the number of faces of the figure, the number of triangles that can be built through points, the number of internal triangles are obtained. The approach of visualization of aggregates is described. The use of color gradation to improve the convenience of perception of the cell in the analysis of the cube cells. It is concluded that the proposed model allows us to perceive each cell as an independent data element in the construction of charts for the analyzed indicators.

scholarly journals A three-dimensional virtual mouse generates synthetic training data for behavioral analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 378-381 ◽  
Author(s):  
Luis A. Bolaños ◽  
Dongsheng Xiao ◽  
Nancy L. Ford ◽  
Jeff M. LeDue ◽  
Pankaj K. Gupta ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Training Data ◽  
Behavioral Analysis ◽  
Virtual Mouse ◽  
Synthetic Training Data

scholarly journals Coronary Centerline Extraction from CCTA Using 3D-UNet

2021 ◽  
Vol 13 (4) ◽  
pp. 101
Author(s):  
Alexandru Dorobanțiu ◽  
Valentin Ogrean ◽  
Remus Brad
Keyword(s):  
Computed Tomography ◽  
Cardiac Computed Tomography ◽  
Three Dimensional ◽  
Class Imbalance ◽  
Training Data ◽  
Cardiac Computed Tomography Angiography ◽  
Centerline Extraction ◽  
Network Training ◽  
Dimensional Reconstruction ◽  
Coronary Centerline

The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals Effects of 3D Printing-Line Directions for Stretchable Sensor Performances

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1791
Author(s):  
Chi Cuong Vu ◽  
Thanh Tai Nguyen ◽  
Sangun Kim ◽  
Jooyong Kim
Keyword(s):  
3D Printing ◽  
Thermoplastic Polyurethane ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Human Motion ◽  
Repeated Measurements ◽  
Fused Filament Fabrication ◽  
And Performance ◽  
The Times ◽  
Stretchable Sensors

Health monitoring sensors that are attached to clothing are a new trend of the times, especially stretchable sensors for human motion measurements or biological markers. However, price, durability, and performance always are major problems to be addressed and three-dimensional (3D) printing combined with conductive flexible materials (thermoplastic polyurethane) can be an optimal solution. Herein, we evaluate the effects of 3D printing-line directions (45°, 90°, 180°) on the sensor performances. Using fused filament fabrication (FDM) technology, the sensors are created with different print styles for specific purposes. We also discuss some main issues of the stretch sensors from Carbon Nanotube/Thermoplastic Polyurethane (CNT/TPU) and FDM. Our sensor achieves outstanding stability (10,000 cycles) and reliability, which are verified through repeated measurements. Its capability is demonstrated in a real application when detecting finger motion by a sensor-integrated into gloves. This paper is expected to bring contribution to the development of flexible conductive materials—based on 3D printing.

scholarly journals 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

2021 ◽  
Vol 11 (2) ◽  
pp. 834
Author(s):  
Marwa Mezghani ◽  
Nicola Hagemeister ◽  
Youssef Ouakrim ◽  
Alix Cagnin ◽  
Alexandre Fuentes ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Knee Osteoarthritis ◽  
Decision Trees ◽  
Exercise Program ◽  
Patient Specific ◽  
Pain Level ◽  
Knee Kinematic ◽  
Kinematic Data ◽  
3D Kinematics ◽  
The Impact

Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.

Examining of the Effects of Target-Oriented Circular Training on Biomotor Features by using Tennis Ball Throwing Machine at 12-14 Age Tennis Performance Sportsmen for 10 weeks

2021 ◽  
Vol 15 (11) ◽  
pp. 3174-3180
Author(s):  
Turgut Çamlibel ◽  
Mehmet Özal
Keyword(s):  
Statistical Significance ◽  
Vertical Jump ◽  
Repeated Measurements ◽  
T Test ◽  
Training Data ◽  
Control Group ◽  
Tennis Ball ◽  
Package Program ◽  
Visual Reaction ◽  
Experimental Group

Aim: In this work, it is aimed that examining the effects of target-oriented circular training on biomotoric features by using a tennis ball throwing machine at 12-14 age tennis performance sportsmen for ten weeks. Method: This research was implemented on sixteen active licensed athletes who played tennis for at least four years in Ankara. The athletes were randomly divided into two separate groups as the experimental group (n=8) and the control group (n=8). After measuring the height, weight, and fat rate of the athletes, biometric tests were started. Flamingo balance, sit-reach, reaction time, five meters and twenty meters sprints, T-test, and standing long jump tests were performed, respectively. Athletes were get heated for ten minutes before the tests and they were given a trying chance. The best scores were recorded by repeating each test twice. In the statistical analysis of the collected data, IBM SPSS 19 package program was used. In repeated measurements, the results were compared by two-way ANOVA with intergroup, intragroup and post-training data. Results: As a result, between the experimental group and control group data; on averages of T-Test (p<0.139), visual reaction (p<0.001), Flexibility (p<0.024), Vertical Jump (p<0.022), Flamingo Balance right foot (p<0.046) and left foot (p<0.045) statistical significance was confirmed. Keywords: Biomotoric features, ITN test, Tennis, Tennis ball machine

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