scholarly journals Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2743 ◽  
Author(s):  
Tanmay Verlekar ◽  
Luís Soares ◽  
Paulo Correia
Keyword(s):  
Stance Phase ◽  
Video Camera ◽  
Step Length ◽  
Support Vector ◽  
Current State ◽  
Gait Features ◽  
Different Types ◽  
Length Step ◽  
Gait Impairments

Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

scholarly journals Customer Segmentation Through Path Reconstruction

2020 ◽  
Author(s):  
Santiago García
Keyword(s):  
Rapid Development ◽  
Customer Segmentation ◽  
Smart Phones ◽  
Indoor Environments ◽  
Flow Conditions ◽  
Gps Receivers ◽  
Current State ◽  
Different Types

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

scholarly journals Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rana Zia Ur Rehman ◽  
Silvia Del Din ◽  
Yu Guan ◽  
Alison J. Yarnall ◽  
Jian Qing Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Decision Making ◽  
Information Gain ◽  
Step Length ◽  
Support Vector ◽  
Step Width ◽  
Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

scholarly journals Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wojciech Wieczorek ◽  
Olgierd Unold
Keyword(s):  
Support Vector Machine ◽  
Correlation Coefficient ◽  
State Of The Art ◽  
Superior Performance ◽  
Support Vector ◽  
Grammatical Inference ◽  
Regular Expressions ◽  
Inference Algorithm ◽  
Current State

The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets.

scholarly journals Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

2019 ◽  
Vol 9 (14) ◽  
pp. 2795 ◽  
Author(s):  
Ahmed Baraka ◽  
Heba Shaban ◽  
Mohamad Abou El-Nasr ◽  
Omneya Attallah
Keyword(s):  
Confusion Matrix ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Measurement Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Tree Classifier ◽  
Different Types

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

scholarly journals Classification of Heart Arrhythmia in ECG Signals using PCA and SVM

2020 ◽  
Vol 9 (10) ◽  
pp. 193-198
Keyword(s):  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Public Database ◽  
Ecg Signals ◽  
Heart Arrhythmia ◽  
Svm Algorithm ◽  
Different Types ◽  
Pca Algorithm

Electro cardiogram (ECG) signals records the vital information about the condition of heart of an individual. In this paper, we are aiming at preparing a model for classification of different types of heart arrhythmia. The MIT-BIH public database for heart arrhythmia has been used in the case of study. There are basically thirteen types of heart arrhythmia. The Principal Component Analysis (PCA) algorithm has been used to collect various important features of heart beats from an ECG signal. Then these features are trained and tested under Support Vector Machine (SVM) algorithm to classify the thirteen classes of heart arrhythmia. In the paper the proposed algorithm has been discussed and the outcome results have been validated. The result shows that the accuracy of our classifier in our research work is more than 91% in most of the cases.

scholarly journals Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

2020 ◽  
Vol 12 (1) ◽  
pp. 127 ◽  
Author(s):  
Hassan Mohamed ◽  
Kazuo Nadaoka ◽  
Takashi Nakamura
Keyword(s):  
Machine Learning ◽  
Video Camera ◽  
Support Vector ◽  
Benthic Habitat ◽  
3D Mapping ◽  
Benthic Habitats ◽  
Significant Information ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

Misfire-misfuel classification using support vector machines

2007 ◽  
Vol 221 (9) ◽  
pp. 1183-1195 ◽  
Author(s):  
E Gani ◽  
C Manzie
Keyword(s):  
Support Vector Machines ◽  
Oxygen Sensor ◽  
Control Unit ◽  
Support Vector ◽  
Exhaust Gas ◽  
Vector Machines ◽  
Recovery Strategies ◽  
Different Types ◽  
The Impact

This paper proposes the use of support vector machines to perform classification between different types of missed combustion event in a six-cylinder engine. On-board diagnostics regulations require the detection of missed combustion events, which is possible through interpretation of crankshaft speed information. However, current approaches provide no information on the actual cause of the event, in particular whether it was caused by a misfuel (absence of fuel) or a misfire (absence of spark) event. Whilst the impact on the environment and emission treatment systems due to misfuel is minimal, misfire events are detrimental to both. Consequently information regarding the causes of missing combustion events potentially allows the development of unique recovery strategies particular to the source of the problem. In this paper, an approach is proposed that will provide the potential for, firstly, detection of a missing combustion event and, secondly, real-time classification of the event into either misfuel or misfire events using feedback from a heated universal exhaust gas oxygen sensor. In order to evaluate the potential of such a system in an engine control unit, a computational complexity measure is also presented.

scholarly journals Gravity-Based Foundations in the Offshore Wind Sector

2019 ◽  
Vol 7 (3) ◽  
pp. 64 ◽  
Author(s):  
M. Esteban ◽  
José-Santos López-Gutiérrez ◽  
Vicente Negro
Keyword(s):  
Water Depth ◽  
High Water ◽  
Offshore Wind ◽  
Is Research ◽  
Current State ◽  
Different Types ◽  
Large Water ◽  
Water Depths ◽  
Offshore Wind Industry

In recent years, the offshore wind industry has seen an important boost that is expected to continue in the coming years. In order for the offshore wind industry to achieve adequate development, it is essential to solve some existing uncertainties, some of which relate to foundations. These foundations are important for this type of project. As foundations represent approximately 35% of the total cost of an offshore wind project, it is essential that they receive special attention. There are different types of foundations that are used in the offshore wind industry. The most common types are steel monopiles, gravity-based structures (GBS), tripods, and jackets. However, there are some other types, such as suction caissons, tripiles, etc. For high water depths, the alternative to the previously mentioned foundations is the use of floating supports. Some offshore wind installations currently in operation have GBS-type foundations (also known as GBF: Gravity-based foundation). Although this typology has not been widely used until now, there is research that has highlighted its advantages over other types of foundation for both small and large water depth sites. There are no doubts over the importance of GBS. In fact, the offshore wind industry is trying to introduce improvements so as to turn GBF into a competitive foundation alternative, suitable for the widest ranges of water depth. The present article deals with GBS foundations. The article begins with the current state of the field, including not only the concepts of GBS constructed so far, but also other concepts that are in a less mature state of development. Furthermore, we also present a classification of this type of structure based on the GBS of offshore wind facilities that are currently in operation, as well as some reflections on future GBS alternatives.

Sign in / Sign up

Export Citation Format

Share Document