A survey of human pilot models for study of Pilot-Induced Oscillation (PIO) in longitudinal aircraft motion

2021 ◽  
pp. 1-14
Author(s):  
J.H. Bidinotto ◽  
H.C. Moura ◽  
J.P.C.A. Macedo
Keyword(s):  
High Gain ◽  
Aviation Safety ◽  
Detection Algorithm ◽  
Human Machine Interaction ◽  
Pilot Model ◽  
High Propensity ◽  
Aircraft Motion ◽  
The Subject ◽  
Machine Interaction ◽  
Different Levels

Abstract Pilot-Induced Oscillation (PIO), although an old issue, still poses a significant threat to aviation safety. The introduction of new systems in modern aircraft modifies the human–machine interaction and makes it necessary for research to revisit the subject from time to time. Given the need of aircraft manufacturers to constantly perform PIO tests, this study analysed the feasibility of using three different computational pilot models (Tustin, Crossover and Precision) to simulate PIO conditions. Three aircraft models with different levels of propensity to PIO (original, low propensity and high propensity) were tested, as well as two pilot gain conditions (normal and high). Data were collected for a purely longitudinal synthetic task through simulations conducted in MATLAB®. PIO conditions were detect using a tuned PIO detection algorithm (ROVER). Data were analysed in terms of both whether the pilot models triggered a PIO condition and for how long the condition was sustained. The results indicated that the three pilot models only provoked PIO conditions when high gain inputs were applied. Additionally, Crossover was the only pilot model to trigger a PIO for the three aircraft models. There were also significant differences between the pilot models in the total PIO time, as the Tustin model typically sustained the oscillatory condition for longer.

scholarly journals Sustainable Compliant Physical Interaction in a Biped-Wheeled Wearable Machine

2020 ◽  
Vol 6 ◽  
Author(s):  
Gaspare Trono ◽  
Angelo Nicolì ◽  
Giovanni Gerardo Muscolo
Keyword(s):  
Physical Interaction ◽  
The Novel ◽  
Functional Design ◽  
Human Machine Interaction ◽  
Multibody Model ◽  
Joint Compliance ◽  
Research Sector ◽  
The World ◽  
The Subject ◽  
Machine Interaction

This paper deals with the problem of the physical human-machine interaction in biped-wheeled exoskeletons and underlines how the symbiosis between humans and machines may increase sustainability. Few exoskeletons in the world are designed with wheels, but the evolution of wearable machines in industries and the convenience of using wheels, underline the importance of the novel research sector of biped-wheeled exoskeletons. This paper shows the functional design and simulation of a novel biped-wheeled wearable machine, including sustainable compliant physical interaction with the subject on board. In particular, the multibody model of the proposed machine is studied and simulated with the subject model on board, including human-machine compliant interactions. The classical human walking cycle is implemented in the machine, varying the speed and the joint compliance of the subject on board and comparing the torque and power output of the motors of the biped-wheeled exoskeleton. The results of this study underline how the joint compliance of the subject on board of the biped-wheeled exoskeleton may influence the efficiency and sustainability of the biped-wheeled wearable machine.

Leveraging human agency to improve confidence and acceptability in human-machine interactions

2021 ◽  
Author(s):  
Valerian Chambon
Keyword(s):  
Sense Of Agency ◽  
Avoidance Task ◽  
Automated Systems ◽  
Human Machine Interaction ◽  
Additional Information ◽  
Positive Effects ◽  
Levels Of Automation ◽  
Machine Interaction ◽  
Positive Effect ◽  
Different Levels

Repeated interactions with automated systems are known to affect how agents experience their own actions and choices. The present study explores the possibility of partially restoring sense of agency in operators interacting with automated systems by providing additional information about how and why these systems make decision. To do so, we implemented an obstacle avoidance task with different levels of automation and explicability. Levels of automation were varied by implementing conditions in which the participant was free or not free to choose which direction to take, whereas levels of explicability were varied by providing or not providing the participant with the system’s confidence in the direction to take. We first assessed how automation and explicability interacted with participants' sense of agency, and then tested whether increased self-agency was systematically associated with greater confidence in the decision and improved system acceptability. The results showed an overall positive effect of system assistance. Providing additional information about the system’s decision (explicability effect) and reducing the cognitive load associated with the decision itself (automation effect) was associated with stronger sense of agency, greater confidence in the decision, and better performance. In addition to the positive effects of system assistance, acceptability scores revealed that participants perceived “explicable” systems more favorably. These results highlight the potential value of studying self-agency in human-machine interaction as a guideline for making automation technologies more acceptable and, ultimately, improving the usefulness of these technologies.

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.

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