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About 10,000 papers for “human-machine interaction”

Human Machine Interaction

Eureka! · 2020

Why do we need greater collaboration between man and machine? Tom Austin-Morgan investigates.

Cited by 2

HUMAN MACHINE INTERACTION

Vimmi Pandey, Neeta Nathai · 2024

Human Machine Interaction (HMI) is the study of the interaction between humans and machines, and how these interactions can be optimized to create more intuitive, efficient, and effective interfaces. HMI encompasses a wide range of technologies and disciplines, including computer science, engineering, psychology, and design. At its core, HMI is concerned with creating interfaces that allow humans to interact with machines in a way that is natural and intuitive, using modalities such as touch screens, voice recognition, augmented reality, virtual reality, AI/ML. The goal of HMI is to make machines more accessible and usable for humans, while also improving the performance and functionality of the machines themselves. HMI is an area of rapid innovation and development, with new technologies and techniques being developed all the time. Some of the key trends in HMI include the use of natural language processing, wearable and IoT, emotion recognition, brain-computer interfaces, and explainable AI. The applications of HMI are wideranging, encompassing everything from consumer electronics such as smart phones and tablets to industrial automation systems and medical devices. By creating more intuitive and effective interfaces between humans and machines, HMI has the potential to revolutionize the way we interact with technology and the world around us

Cited by 0

Human-Machine Interaction

Ananya Pandey, Jipson Joseph · Advances in computational intelligence and robotics book series · 2025

In the era of digitalization and technological advancement it is crucial to note the dichotomy between the earlier and present times with regard to human-machine interaction. Previously human controlled machines but today its opposite happens. Human-Machine Interface has so deeply touched the life of the present generation and its most explicit example could be the change from a basic push button to a touch-screen display. A simple touch has multiple effects on people's daily life. Innovation has permeated and influenced daily physical activities. Every time people use an ATM, food dispenser, snack machine, touchscreen, cloud computing, etc., they inevitably make interactions between humans and machines. Since the applications of HMI cover numerous fields, several ethical and legal considerations should be taken into account for making it effective and viable. In this perspective, the authors analyze the challenges of Human Machine Interaction suggesting a concrete policy design for its effective application.

Cited by 2

Human-Machine Interaction

Industrial Vehicle Technology International · 2018

MULTIMODAL HUMAN-MACHINE INTERFACES ARE READJUSTING NOTIONS OF WHAT IS POSSIBLE FOR OPERATORS IN THE WAY INDUSTRIAL VEHICLES ARE CONTROLLED

Cited by 0

Human-Machine Trust Interaction

Conghui Liu · International Journal of Dependable and Trustworthy Information Systems · 2010

Improving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses the current state, challenges, problems and limitations of establishing and improving the user’s trust in human-machine interaction. Finally, the author summarizes and evaluates the existing solutions for improving the user’s trust appropriately in e-commerce environment.

Cited by 4

Informational human-machine interaction

Sabah Al‐Fedaghi · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008

In the field of human-computer interaction studies, the need exists to understand how computers can be made more effective in cooperating with people. Difficulties are caused by lack of a general model for the flow of information, actions, and psychological aspects between humans and computers. This paper proposes an information-based model of human-computer interaction that takes into consideration the nature of the things that flow between them. The resultant model provides a basic framework for studying various notions in the area of human-computer interaction.

Cited by 2

From human – machine interaction to human – machine cooperation

Jean-Michel Hoc · Ergonomics · 2000

Since the 1960s, the rapid growth of information systems has led to the wide development of research on human-computer interaction (HCI) that aims at the designing of human-computer interfaces presenting ergonomic properties, such as friendliness, usability, transparency, etc. Various work situations have been covered--clerical work, computer programming, design, etc. However, they were mainly static in the sense that the user fully controls the computer. More recently, public and private organizations have engaged themselves in the enterprise of managing more and more complex and coupled systems by the means of automation. Modern machines not only process information, but also act on dynamic situations as humans have done in the past, managing stock exchange, industrial plants, aircraft, etc. These dynamic situations are not fully controlled and are affected by uncertain factors. Hence, degrees of freedom must be maintained to allow the humans and the machine to adapt to unforeseen contingencies. A human-machine cooperation (HMC) approach is necessary to address the new stakes introduced by this trend. This paper describes the possible improvement of HCI by HMC, the need for a new conception of function allocation between humans and machines, and the main problems encountered within the new forms of human-machine relationship. It proposes a conceptual framework to study HMC from a cognitive point of view in highly dynamic situations like aircraft piloting or air-traffic control, and concludes on the design of 'cooperative' machines.

Cited by 371

Machine learning for human-machine interaction

Zhou, Yufeng · DR-NTU (Nanyang Technological University) · 2024

The rise of collaborative robots has transformed human lives, introducing more flexible and efficient robotic interaction and automation. This has opened new possibilities for human-robot collaborations, which are poised to play a crucial role in future manufacturing industries and robots-for-humans applications. Recent
\nresearch has enabled promising approaches in various domains, including procedure learning from instructional videos, key action segmentation, and human action anticipation. However, these approaches typically rely on extensive data collection and lack direct applicability to human-robot collaborations, where examples are limited, and situational requirements vary. Existing robots are often constrained by their limited task-specific designs, lacking the adapt ability to different scenarios and the intelligence required for effective collaboration with humans.
\nVisual-based imitation learning, taking advantage of the rapid development of deep learning, provides a framework for learning complex manipulation skills by leveraging human demonstrations. This dissertation aims to analyze these challenges by studying a series of machine vision and learning algorithms which can enable robots to learn and model the key steps involved in manufacturing and robots-for-humans tasks and empower robots with the capability to understand human intentions and behaviors. Specifically, this dissertation studies a self-supervised visual representation module and analyzes the feasibility of a robust
\nand customizable learning agent that can adapt itself to different human-robot collaboration scenarios and tasks. The following are the main research findings of this dissertation: (1) To derive meaningful visual representations from the provided demonstrations, a visual representation model is pre-trained. The visual representation was obtained by taking advantage of a combination of existing techniques, including Time
\nContrastive Learning (TCL) and Masked Autoencoder (MAE), utilizing a large-scale dataset which consists of frames from the Internet and egocentric videos. This pre-trained visual representation serves as an efficient perception head for subsequent stages of the imitation learning process. By employing this approach, a solid foundation is established for robust and effective perception-based learning. (2) The utilization of multiple established imitation policies is leveraged to assess the efficacy of a robot learning agent integrated with an efficient self-supervised representation module. This evaluation is conducted within the context of simulated robot manipulation tasks, with the intention of achieving proficient performance using a limited number of demonstrations. Through the validation of human-robot
\ncollaboration via the integration of machine vision and learning techniques, this dissertation attempts to explore the possibility of collaborative robots in industrial manufacturing and robots-for-humans applications.

Cited by 0

Biometrics in human-machine interaction

Warren Adam Shier, Svetlana Yanushkevich · 2015

This paper evaluates the role of biometrics in human-machine interactions in applications such as decision-making, interview support systems and human-robot interfacing. Presently, there are biometrics projects that utilize the “talking face” technology. We forecast that future systems will verify humans using their biometrics, analyze facial expressions, temperature and blood pressure of the human, as well as generate cognitive questions and analyze answers. This paper presents a survey of the related work, and provides a vision of future generations of complex biometric-based human-machine systems and their applications.

Cited by 10