scholarly journals Emotions in Virtual Reality

2021 ◽  
Author(s):  
Darlene Barker ◽  
Haim Levkowitz
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Emotion Recognition ◽  
Nonverbal Cues ◽  
Haptic Devices ◽  
The One

One of the first senses we learn about at birth is touch, and the one sense that can deepen our experience of many situations is touch. In this paper we propose the use of emotions including touch within virtual reality (VR) to create a simulated closeness that currently can only be achieved with in-person interactions and communications. With the simulation of nonverbal cues, we can enhance a conversation or interaction in VR. Using haptic devices to deliver the simulation of touch between users via sensors and machine learning for emotion recognition based on data collected; all working towards simulated closeness in communication despite distance or being in VR. We present a direction for further research on how to simulate inperson communication within VR with the use of emotion recognition and touch to achieve a close-to-real interaction.

scholarly journals A Touching Sensation

2003 ◽  
Vol 125 (11) ◽  
pp. 30-32 ◽  
Author(s):  
Jean Thilmany
Keyword(s):  
Virtual Reality ◽  
Physical Therapy ◽  
Force Feedback ◽  
Virtual Reality Technology ◽  
Haptic Devices ◽  
University Of Utah ◽  
Robotic Devices ◽  
The One ◽  
The University ◽  
Sense Of Touch

This article discusses Haptics technology that is being used to train surgeons and rehabilitate patients. Haptics technology, a recent enhancement to virtual reality technology, gives users the touch and feel of simulated objects they interact with, usually through a device like a specialized mouse or a haptic glove. John Hollerbach, a computing professor and an adjunct professor of mechanical engineering at the University of Utah, says haptic devices and robotic devices share the same drawbacks, particularly involving limits to the miniaturization of motors. Haptic devices that fit the hand, like the one sold by Immersion Corp., or the force-feedback glove developed at Rutgers give the wearer a sense of touch, as if one is squeezing a ball or tracing an object. Hollerbach of the University of Utah said the future looks bright for haptics. The Rutgers ankle simulates walking over several types of terrain for patients undergoing physical therapy. Haptics can simulate assembling a part to ensure that it is designed for easy construction.

scholarly journals Analysis of Risk Factors in Dementia Through Machine Learning

2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Algorithms ◽  
Progressive Increase ◽  
Control Group ◽  
Data Sets ◽  
Modifiable Factors ◽  
Validation Set ◽  
The One ◽  
And Control ◽  
Potential Tool

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Rehabilitation Services ◽  
Simulation Scenario ◽  
Common Problems ◽  
Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

scholarly journals Virtual Reality Gait Training to Promote Balance and Gait Among Older People: A Randomized Clinical Trial

Geriatrics ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Kyeongjin Lee
Keyword(s):  
Virtual Reality ◽  
The Elderly ◽  
Gait Training ◽  
Control Group ◽  
Timed Up And Go ◽  
Elderly Individuals ◽  
Functional Reach Test ◽  
Balance Ability ◽  
Standing Test ◽  
The One

Falls are the leading cause of injury and injury-related death in the elderly. This study evaluated the effect of virtual reality gait training (VRGT) with non-motorized treadmill on balance and gait ability of elderly individuals who had experienced a fall. Fifty-six elderly individuals living in local communities participated in this study. Subjects who met the selection criteria were randomly divided into a VRGT group (n = 28) and a control group (n = 28). The VRGT group received VRGT with non-motorized treadmill for 50 min a day for 4 weeks and 5 days a week. The control group received non-motorized treadmill gait training without virtual reality for the same amount of time as the VRGT group. Before and after the training, the one-leg-standing test, Berg Balance Scale, Functional Reach test, and Timed Up and Go test were used to assess balance ability, and the gait analyzer system was used to evaluate the improvement in gait spatiotemporal parameters. In the VRGT group, the balance ability variable showed a significant decrease in the one-leg-standing test and a significant improvement in the Timed Up and Go test. With respect to spatiotemporal gait parameters, velocity and step width decreased significantly in the VRGT group (p < 0.05), and stride length and step length were significantly improved in the VRGT group (p < 0.05). VRGT with non-motorized treadmill has been shown to improve balance and gait ability in the elderly. This study is expected to provide basic data on exercise programs for the elderly to prevent falls.

scholarly journals Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5135
Author(s):  
Ngoc-Dau Mai ◽  
Boon-Giin Lee ◽  
Wan-Young Chung
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Temporal Lobe ◽  
Affective Computing ◽  
Electrode Placement ◽  
Entropy Measure ◽  
Support Vector ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Computing Method

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

scholarly journals Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

2020 ◽  
Vol 58 (6) ◽  
pp. 1357-1367 ◽  
Author(s):  
Samaneh Siyar ◽  
Hamed Azarnoush ◽  
Saeid Rashidi ◽  
Alexander Winkler-Schwartz ◽  
Vincent Bissonnette ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Tumor Resection ◽  
Skill Levels
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