scholarly journals Workplace Learning through Human‐Machine Interaction in a Transient Multilingual Blue‐Collar Work Environment

2020 ◽  
Vol 30 (3) ◽  
pp. 369-388
Author(s):  
Daan Hovens
Keyword(s):  
Work Environment ◽  
Workplace Learning ◽  
Blue Collar ◽  
Human Machine Interaction ◽  
Machine Interaction

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

Holistic, Context-sensitive Human-machine Interaction for Automated Vehicles

ATZ worldwide ◽  
2021 ◽  
Vol 123 (3) ◽  
pp. 46-49
Author(s):  
Tobias Hesse ◽  
Michael Oehl ◽  
Uwe Drewitz ◽  
Meike Jipp
Keyword(s):  
Automated Vehicles ◽  
Human Machine Interaction ◽  
Context Sensitive ◽  
Machine Interaction

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

scholarly journals Human Factors Modelling Approach: Application to a Safety Device Supporting Crane Operations in Major Hazard Industries

2021 ◽  
Vol 13 (4) ◽  
pp. 2304
Author(s):  
Maria Francesca Milazzo ◽  
Giuseppa Ancione ◽  
Giancarlo Consolo
Keyword(s):  
Systematic Approach ◽  
New Technologies ◽  
Health And Safety ◽  
Safety Device ◽  
Human Machine Interaction ◽  
Safety And Health ◽  
Emotional Strain ◽  
Smart Safety ◽  
Machine Interaction ◽  
Modelling Approach

The European Directive on Safety and Health at Work and the following normatives have the scope to provide high levels of health and safety at work, based on some general principles managing activities and including the risk assessment to continuously improve processes and workplaces. However, the working area changes and brings new risks and challenges for workers. Several of them are associated with new technologies, which determine complex human–machine interactions, leading to an increased mental and emotional strain. To reduce these emerging risks, their understanding and assessment are important. Although great efforts have already been made, there is still a lack of conceptual frameworks for analytically assessing human–machine interaction. This paper proposes a systematic approach that, beyond including the classification in domains to explain the complexity of the human–machine interaction, accounts for the information processing of the human brain. Its validation is shown in a major accident hazard industry where a smart safety device supporting crane related operations is used. The investigation is based on the construction of a questionnaire for the collection of answers about the feeling of crane operators when using the device and the evaluation of the Cronbach’s alpha to measure of the reliability of the assessment.

scholarly journals Quantifying the Predictability of Visual Scanpaths Using Active Information Storage

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 167
Author(s):  
Patricia Wollstadt ◽  
Martina Hasenjäger ◽  
Christiane B. Wiebel-Herboth
Keyword(s):  
Information Storage ◽  
Gaze Behavior ◽  
Behavioral Experiments ◽  
Human Machine Interaction ◽  
Cognitive States ◽  
Visual Scanpath ◽  
Active Information Storage ◽  
Temporal Horizon ◽  
Machine Interaction ◽  
Transition Entropy

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of a visual scanpath as the entropy of transitions between fixations and has been shown to correlate with changes in task demand or changes in observer state. Measuring scanpath predictability is thus a promising approach to identifying viewers’ cognitive states in behavioral experiments or gaze-based applications. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate the actual predictability of the current fixation given past gaze behavior. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes’ multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human–machine interaction.

Sign in / Sign up

Export Citation Format

Share Document