scholarly journals A Typo-morphological Approach to Human–Machine Interaction Analysis in Music

2017 ◽  
Vol 22 (3) ◽  
pp. 315-323 ◽  
Author(s):  
Eric Maestri
Keyword(s):  
Interaction Analysis ◽  
Sound Sources ◽  
Human Machine Interaction ◽  
Conceptual Tool ◽  
Electronic Sound ◽  
Musical Perception ◽  
Musical Thinking ◽  
Analytical Perspective ◽  
Music Information ◽  
Machine Interaction

This article proposes an analytical perspective on the aural analysis of human–machine interaction. Starting from Pierre Schaeffer’s musical thinking, it focuses on the perception of sound morphologies that generate from human–machine interaction. This interaction, usually interpreted under the perspective of the devices and technological innovation, will be interpreted through the perspective of musical perception and semantics. In the article, the author applies the criterion of ‘allure’, that permits the analysis of the sound object’s sustain and indicates the sound agent as mechanical, living or natural. ‘Allure’ is used as a theoretical framework for the analysis of human–computer interaction (HCI) in music. Inspired by a hypothetical perceptive Turing’s test, the author employs the ‘allure’ criterion as a conceptual tool for an aural analysis of human–machine interaction: aural analysis can indeed reveal the interaction of sound agents through the cognitive mechanism of motor imagery. This approach can be extended and developed in music information retrieval. The author will develop this reflection showing the actuality of Schaeffer’s perspective in HCI analysis. Finally, to highlight the interaction between human and electronic sound sources, the author will consider a case study: the first movement of Traiettoria for piano and electronics by Marco Stroppa.

scholarly journals Transdisciplinary Assessment Matrix to Design Human-Machine Interaction

2020 ◽  
Author(s):  
Fabio Grandi ◽  
Margherita Peruzzini ◽  
Roberto Raffaeli ◽  
Marcello Pellicciari
Keyword(s):  
User Experience ◽  
Interaction Analysis ◽  
Mental Workload ◽  
Physiological Data ◽  
Human Machine Interaction ◽  
Simulated Environments ◽  
Assessment Matrix ◽  
Physical Comfort ◽  
Machine Interaction

Successful interaction with complex systems is based on the system ability to satisfy the user needs during interaction tasks, mainly related to performances, physical comfort, usability, accessibility, visibility, and mental workload. However, the “real” user experience (UX) is hidden and usually difficult to detect. The paper proposes a Transdisciplinary Assessment Matrix (TAS) based on collection of physiological, postural and visibility data during interaction analysis, and calculation of a consolidated User eXperience Index (UXI). Physiological data are based on heart rate parameters and eye pupil dilation parameters; postural data consists of analysis of main anthropometrical parameters; and interaction data from the system CAN-bus. Such a method can be adopted to assess interaction on field, during real task execution, or within simulated environments. It has been applied to a simulated case study focusing on agricultural machinery control systems, involving users with a different level of expertise. Results showed that TAS is able to validly objectify UX and can be used for industrial cases.

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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