scholarly journals Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1039 ◽  
Author(s):  
Justas Šalkevicius ◽  
Robertas Damaševičius ◽  
Rytis Maskeliunas ◽  
Ilona Laukienė
Keyword(s):  
Virtual Reality ◽  
Mental Stress ◽  
Cross Validation ◽  
Detection System ◽  
Anxiety Level ◽  
Physiological Signals ◽  
Support Vector ◽  
Svm Classifier ◽  
Stress Detection ◽  
The Individual

Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

scholarly journals Support Vector Machine based Stress Detection System to manage COVID-19 pandemic related stress from ECG signal

2021 ◽  
Vol 20 (1) ◽  
pp. 8-16
Author(s):  
Md Fahim Rizwan ◽  
Rayed Farhad ◽  
Md. Hasan Imam
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Health Condition ◽  
Cardiac Patients ◽  
Gaussian Kernel ◽  
Training Dataset ◽  
Support Vector ◽  
Portable Devices ◽  
Stress Detection ◽  
Specialist Doctor

This study represents a detailed investigation of induced stress detection in humans using Support Vector Machine algorithms. Proper detection of stress can prevent many psychological and physiological problems like the occurrence of major depression disorder (MDD), stress-induced cardiac rhythm abnormalities, or arrhythmia. Stress induced due to COVID -19 pandemic can make the situation worse for the cardiac patients and cause different abnormalities in the normal people due to lockdown condition. Therefore, an ECG based technique is proposed in this paper where the ECG can be recorded for the available handheld/portable devices which are now common to many countries where people can take ECG by their own in their houses and get preliminary information about their cardiac health. From ECG, we can derive RR interval, QT interval, and EDR (ECG derived Respiration) for developing the model for stress detection also. To validate the proposed model, an open-access database named "drivedb” available at Physionet (physionet.org) was used as the training dataset. After verifying several SVM models by changing the ECG length, features, and SVM Kernel type, the results showed an acceptable level of accuracy for Fine Gaussian SVM (i.e. 98.3% for 1 min ECG and 93.6 % for 5 min long ECG) with Gaussian Kernel while using all available features (RR, QT, and EDR). This finding emphasizes the importance of including ventricular polarization and respiratory information in stress detection and the possibility of stress detection from short length data(i.e. form 1 min ECG data), which will be very useful to detect stress through portable ECG devices in locked down condition to analyze mental health condition without visiting the specialist doctor at hospital. This technique also alarms the cardiac patients form being stressed too  much which might cause severe arrhythmogenesis.

The Animal Classification: An Evaluation of Different Transfer Learning Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 27-31
Author(s):  
Ken-ji Ee ◽  
Ahmad Fakhri Bin Ab. Nasir ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Nur Hafieza Ismail
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Classification Accuracy ◽  
Validation Technique ◽  
Search Approach ◽  
Fold Cross Validation

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.

scholarly journals A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

2018 ◽  
Vol 7 (3.12) ◽  
pp. 521 ◽  
Author(s):  
Pathanjali C ◽  
Vimuktha E Salis ◽  
Jalaja G ◽  
Latha A
Keyword(s):  
Comparative Study ◽  
Detection System ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Nearest Neighbour ◽  
Classification Models ◽  
Food Detection ◽  
Knn Classifier ◽  
Indian Food

Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier. 

AMPpred: An On-Line Predictor Design for Automated AMP Recognition

2012 ◽  
Vol 229-231 ◽  
pp. 2276-2279
Author(s):  
Yu An Pan ◽  
Xuan Xiao ◽  
Pu Wang
Keyword(s):  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Bioinformatics Tool ◽  
Physical Chemical ◽  
Novel Approach ◽  
Cross Covariance ◽  
On Line ◽  
Leave One Out ◽  
User Friendly

Antimicrobial peptides (AMP) are potent, broad spectrum antibiotics which demonstrate potential as novel therapeutic agents. Because it is both time-consuming and laborious to identify new AMPs by experiment, this paper tries to resolve this problem by pattern recognition. Two major contents included: Firstly, up to six kinds of physicochemical properties value are selected to code the AMP sequence as physical-chemical property matrix (PCM), then auto and cross covariance transformation is performed to extract features from the PCM for AMP sequence expression; Secondly, these feature vectors are input to a powerful Support Vector Machine (SVM) classifier for training and new query AMP recognition. For a newly constructed AMP benchmark dataset, the overall classification accuracy about 96% has been achieved through the rigorous Leave-One-Out cross-validation. For convenience, a user-friendly web server, AMPpred, has been established at http://icpr.jci.jx.cn/bioinfo/AMPpred. It is anticipated that this on-line predictor may become a useful bioinformatics tool for molecular biology and drug development. Also, its novel approach will further stimulate the development of predicting peptide attributes.

scholarly journals An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

2021 ◽  
Vol 40 (2) ◽  
pp. 279-297
Author(s):  
Faria Nazir ◽  
Muhammad Nadeem Majeed ◽  
Mustansar Ali Ghazanfar ◽  
Muazzam Maqsood
Keyword(s):  
Language Learning ◽  
Second Language Learners ◽  
Positive Impact ◽  
Detection System ◽  
Confusion Matrix ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Assisted ◽  
Detection Model

Over the last few decades, the field of artificial intelligence and machine learning has evolved. Due to the advancement in these fields, much work has been done to assist language learning with the help of computers called Computer-Assisted Language Learning (CALL). Mispronunciation detection is one of the significant tasks of the CALL system. An efficient mispronunciation detection model has a positive impact on the life of second language learners by providing phoneme level feedback. In this paper, we introduce the phone grouping technique for mispronunciation detection that is based on mistakes probability. We consider mispronunciation detection as a classification problem, traditionally for this purpose, a separate classifier is trained for each phoneme mistake that requires a lot of memory and time. Instead of training a separate classifier, we group the phoneme based on their mistakes probability that helps in reducing the number of the classifiers to be trained and also saves memory and time. We use the Support Vector Machine (SVM) classifier and test the results on the Arabic dataset (28 Phonemes). The performance of our proposed method is evaluated by using accuracy. The results of the model are evaluated using the confusion matrix and gives an accuracy of 88%. Our approach outperforms the existing systems developed for Arabic phonemes in terms of accuracy and is also time/memory efficient.

Glaucoma Detection using Tetragonal Local Octa Patterns and SVM from Retinal Images

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Momina Masood
Keyword(s):  
Detection System ◽  
Screening Method ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Recognition ◽  
Glaucoma Detection ◽  
Retinal Fundus Image ◽  
Quantitative Results ◽  
Independent Features

Glaucoma is a fatal disease caused by the imbalance of intraocular pressure inside the eye which can result in lifetime blindness of the victim. Efficient screening systems require experts to manually analyze the images to recognize the disease. However, the challenging nature of the screening method and lack of trained human resources, effective screening-oriented treatment is an expensive task. The automated systems are trying to cope with these challenges; however, these methods are not generalized well to large datasets and real-world scenarios. Therefore, we have introduced an automated glaucoma detection system by employing the concept of the Content-Based Image Retrieval (CBIR) domain. The Tetragonal Local Octa Pattern (T-LOP) is used for features computation which is employed to train the SVM classifier to show the technique significance. We have evaluated our method over challenging datasets namely, Online Retinal Fundus Image (ORIGA) and High-Resolution Fundus (HRF). Both the qualitative and quantitative results show that our technique outperforms the latest approaches due to the effective localization power of T-LOP as it computes the anatomy independent features and ability of Support Vector Machine (SVM) to deal with over-fitted training data. Therefore, the presented technique can play an important role in the automated recognition of glaucoma lesions and can be applied to other medical diseases as well.

A Research of Virus Detection Combined Dynamic and Static Analysis Methods

2011 ◽  
Vol 187 ◽  
pp. 625-630
Author(s):  
Chun Yu Miao ◽  
Li Na Chen
Keyword(s):  
Neural Network ◽  
Static Analysis ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
Virus Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Analysis Methods ◽  
Individual Classifier ◽  
Theory Of Evidence

we present a virus detection system based on the D-S theory of evidence, in which the dynamic and static analysis methods are combined. The detection engine applies two types of classifier, support vector amchine and probabilistic neural network to detect the virus. For SVM classifier, we extract the feature vector by monitoring the samples. And the static feature of samples is used in the probabilistic neural network classifier. Finally, the D-S theory of evidence is used to combine the contribution of each individual classifier to give the final decision.experiments show the presented method is more efficiently of the virus detections.

scholarly journals A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

2011 ◽  
Vol 58 (10) ◽  
pp. 4857-4865 ◽  
Author(s):  
Alberto de Santos Sierra ◽  
Carmen Sanchez Avila ◽  
Javier Guerra Casanova ◽  
Gonzalo Bailador del Pozo
Keyword(s):  
Fuzzy Logic ◽  
Detection System ◽  
Physiological Signals ◽  
Stress Detection
Sign in / Sign up

Export Citation Format

Share Document