Real-time Stress Detection Model and Voice Analysis: An Integrated VR-based Game for Training Public Speaking Skills

2021 ◽  
Author(s):  
Arushi ◽  
Roberto Dillon ◽  
Ai Ni Teoh
Keyword(s):  
Real Time ◽  
Public Speaking ◽  
Stress Detection ◽  
Voice Analysis ◽  
Detection Model ◽  
Time Stress ◽  
Speaking Skills

Toward soft real-time stress detection using wrist-worn devices for human workspaces

2020 ◽  
Author(s):  
Sunder Ali Khowaja ◽  
Aria Ghora Prabono ◽  
Feri Setiawan ◽  
Bernardo Nugroho Yahya ◽  
Seok-Lyong Lee
Keyword(s):  
Real Time ◽  
Stress Detection ◽  
Time Stress

scholarly journals Real-Time Stress Detection by Means of Physiological Signals

10.5772/18246 ◽  
2011 ◽  
Author(s):  
Alberto De ◽  
Carmen Snchez-Avila ◽  
Javier Guerra-Casanova ◽  
Gonzalo Bailador-Del
Keyword(s):  
Real Time ◽  
Physiological Signals ◽  
Stress Detection ◽  
Time Stress

A Bio-Inspired Reservoir-Computer for Real-Time Stress Detection From ECG Signal

2020 ◽  
Vol 3 ◽  
pp. 290-293
Author(s):  
Sanjeev Tannirkulam Chandrasekaran ◽  
Sumukh Prashant Bhanushali ◽  
Imon Banerjee ◽  
Arindam Sanyal
Keyword(s):  
Real Time ◽  
Ecg Signal ◽  
Stress Detection ◽  
Time Stress

Real-time stress detection of monocrystalline silicon by laser irradiation using Mach–Zehnder interferometry

Pramana ◽  
2016 ◽  
Vol 87 (6) ◽  
Author(s):  
MING GUO ◽  
GUANGYONG JIN ◽  
YONG TAN ◽  
WEI ZHANG ◽  
MINGXIN LI ◽  
...  
Keyword(s):  
Real Time ◽  
Laser Irradiation ◽  
Monocrystalline Silicon ◽  
Stress Detection ◽  
Time Stress

scholarly journals Beyond Reality—Extending a Presentation Trainer with an Immersive VR Module

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3457 ◽  
Author(s):  
Schneider ◽  
Romano ◽  
Drachsler
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
User Experience ◽  
Positive Attitude ◽  
Formative Evaluation ◽  
Public Speaking ◽  
Level Of Satisfaction ◽  
Feedback Module ◽  
Speaking Skills

The development of multimodal sensor-based applications designed to support learners with the improvement of their skills is expensive since most of these applications are tailor-made and built from scratch. In this paper, we show how the Presentation Trainer (PT), a multimodal sensor-based application designed to support the development of public speaking skills, can be modularly extended with a Virtual Reality real-time feedback module (VR module), which makes usage of the PT more immersive and comprehensive. The described study consists of a formative evaluation and has two main objectives. Firstly, a technical objective is concerned with the feasibility of extending the PT with an immersive VR Module. Secondly, a user experience objective focuses on the level of satisfaction of interacting with the VR extended PT. To study these objectives, we conducted user tests with 20 participants. Results from our test show the feasibility of modularly extending existing multimodal sensor-based applications, and in terms of learning and user experience, results indicate a positive attitude of the participants towards using the application (PT+VR module).

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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