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Journal articles on the topic 'Human machine interaction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Prepin, Ken, and Arnaud Revel. "Human–machine interaction as a model of machine–machine interaction: how to make machines interact as humans do." Advanced Robotics 21, no. 15 (2007): 1709–23. http://dx.doi.org/10.1163/156855307782506192.

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2

Hoc, Jean-Michel. "From human – machine interaction to human – machine cooperation." Ergonomics 43, no. 7 (2000): 833–43. http://dx.doi.org/10.1080/001401300409044.

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3

Imamguluyev, Rahib, Tunzala Imanova, Parvana Hasanova, et al. "Revolutionizing Human-Machine Interaction: Fuzzy Logic in interface Design." International Journal of Research Publication and Reviews 5, no. 8 (2024): 2939–49. http://dx.doi.org/10.55248/gengpi.5.0824.2155.

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4

Liu, Conghui. "Human-Machine Trust Interaction." International Journal of Dependable and Trustworthy Information Systems 1, no. 4 (2010): 61–74. http://dx.doi.org/10.4018/jdtis.2010100104.

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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 t

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Meyer, Joachim, Chris Miller, Peter Hancock, Ewart J. de Visser, and Michael Dorneich. "Politeness in Machine-Human and Human-Human Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 279–83. http://dx.doi.org/10.1177/1541931213601064.

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Computers communicate with humans in ways that increasingly resemble interactions between humans. Nuances in expression and responses to human behavior become more sophisticated, and they approach those of human-human interaction. The question arises whether we want systems eventually to behave like humans, or whether systems should, even when much more developed, still adhere to rules that are different from the rules governing interpersonal communication. The panel addresses this issue from various perspectives, eventually aiming to gain some insights into the question of the direction to wh

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6

Haqqu, Rizca, and Salwa Nur Rohmah. "Interaction Process Between Humans and ChatGPT in the Context of Interpersonal Communication." Jurnal Ilmiah LISKI (Lingkar Studi Komunikasi) 10, no. 1 (2024): 23. http://dx.doi.org/10.25124/liski.v10i1.7216.

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This study examines human interaction with artificial intelligence technology, focusing on the implementation of ChatGPT, a chatbot developed by OpenAI. Through the Human-Machine Communication (HMC) approach, the research describes human-like attributes in ChatGPT, exploring emotional responses and utility in educational, professional, and personal contexts. Qualitative research methods with triangulation techniques were used for a holistic understanding, involving interviews, observations, and document analysis. The results indicate that ChatGPT can provide adaptive responses, adjusting langu

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7

King, Raymond E. "Handbook of human-machine interaction." Aviation, Space, and Environmental Medicine 83, no. 8 (2012): 811. http://dx.doi.org/10.3357/asem.3232.2012.

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8

Okura, Michiko. "Interface for Human-machine Interaction." TRENDS IN THE SCIENCES 10, no. 8 (2005): 52–55. http://dx.doi.org/10.5363/tits.10.8_52.

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9

Gouvrit, Florence. "Empathy and Human-Machine Interaction." International Journal of Synthetic Emotions 4, no. 2 (2013): 8–21. http://dx.doi.org/10.4018/ijse.2013070102.

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This paper presents the framework of the author’s practice and research exploring empathy and human-machine interaction in projects involving robotic art and video installations and performance. The works investigate emotions and embodiment, presence and absence, relationships and loss, and ways to implicate these ideas in encounters between technology-based artwork and the viewer.

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NEWELL, CHRISTOPHER, ALISTAIR D. N. EDWARDS, and PAUL CAIRNS. "‘Liveness’ in human-machine interaction." International Journal of Performance Arts and Digital Media 7, no. 2 (2011): 221–37. http://dx.doi.org/10.1386/padm.7.2.221_1.

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11

Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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12

Komal, Khalid, Rahim Mohammad, Malik Samman, et al. "Human-Machine Interaction in the Age of Artificial Intelligence: Ethical, Technical and Social Implications." Global Scientific and Academic Research Journal of Multidisciplinary Studies 3, no. 11 (2024): 93–101. https://doi.org/10.5281/zenodo.14228435.

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<em>Artificial Intelligence (AI) has significantly advanced the field of Human-Machine Interaction (HMI), enhancing communication, accessibility and responsiveness. Artificial agents have become increasingly prevalent in human social life. However, this rapid development brings complex ethical, technical, and social challenges that impact privacy, bias, and human autonomy. Human-machine interaction (HMI) is the study of how people and machines communicate and work together, and artificial intelligence (AI) is the field of computer science that aims to create machines that can perform tasks tha

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13

V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.

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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and

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14

M.M., Khoshyaran. "Human-Machine Interaction: Causal Dynamical Networks." Journal of Progressive Research in Mathematics 12, no. 1 (2017): 1789–802. https://doi.org/10.5281/zenodo.3975947.

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The objective of this paper is to introduce a modified version of the Causal Dynamical Networks (CDN) algorithm for application in the human-machine interaction. It is demonstrated that an individual does not interact with one robot, but with a multitude of personalities stored in the robot. These personalities are independent of each other. A robot thus does not have a unique personality. In order for a robot to become a unique individual a new algorithm is proposed. The new algorithm is called the Causal Form Fluctuation Network (CEFN). It is shown that such an algorithm can help machines de

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15

Komarova, V., J. Lonska, V. Tumalavičius, and A. Krasko. "Artificial sociality in the human-machine interaction." RUDN Journal of Sociology 21, no. 2 (2021): 377–90. http://dx.doi.org/10.22363/2313-2272-2021-21-2-377-390.

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The article aims at clarifying the concept artificial sociality in the human-machine interaction by answering the question whether artificial sociality is a prerequisite or a result of this interaction. The authors conducted a logical analysis of the definitions of sociality and artificial sociality as presented in the scientific literature, and conducted an empirical study of artificial sociality in the human-machine interaction with three methods - comparison of means, correlation analysis and discriminant analysis. All three methods were used in the analysis of the same data: indicators of

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16

Modi, Nandini, and Jaiteg Singh. "Role of Eye Tracking in Human Computer Interaction." ECS Transactions 107, no. 1 (2022): 8211–18. http://dx.doi.org/10.1149/10701.8211ecst.

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With the invention of computers arises the need of an interface for users and interacting with a computer has become a natural practice. For all the opportunities a machine can bring, it is now a limiting factor for humans and their interaction with machines. This has given rise to a significant amount of research in the area of human computer interaction to make it more intuitive, simpler, and efficient. Human interaction with computers is no longer confined to printers and keyboards. Traditional input devices give way to natural inputs like voice, gestures, and visual computing using eye tra

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17

Andrist, Sean, Dan Bohus, Bilge Mutlu, and David Schlangen. "Turn-Taking and Coordination in Human-Machine Interaction." AI Magazine 37, no. 4 (2017): 5–6. http://dx.doi.org/10.1609/aimag.v37i4.2700.

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This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination

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18

Stanley, Jeff, Ozgur Eris, and Monika Lohani. "A Conceptual Framework for Machine Self-Presentation and Trust." International Journal of Humanized Computing and Communication 2, no. 1 (2021): 20–45. http://dx.doi.org/10.35708/hcc1869-148366.

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Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We

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19

Chen, Shuxian, Zongqiang Ren, Xikai Yu, and Ao Huang. "A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/8584888.

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The increasing use of smart machines and devices is not only changing production principles but also reshaping the value of cocreation logic. The interaction between human and smart machine is the enabler of generating augmented intelligence. A system dynamics model is abstracted from smart manufacturing practices to represent the evolutionary processes of inertia, capability, and reliability induced by human-machine interaction. Human-machine interaction is conceptualized into two dimensions: technical and cognitive interaction. Simulation experiments illustrate how the improvement of human-m

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20

Li, Jie. "Exploring a Human-Machine Interaction Method." International Journal of High School Research 2, no. 3 (2020): 1–6. http://dx.doi.org/10.36838/v2i3.1.

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21

Alm, Torbjorn, Jens Alfredson, and Kjell Ohlsson. "Simulator-based human-machine interaction design." International Journal of Vehicle Systems Modelling and Testing 4, no. 1/2 (2009): 1. http://dx.doi.org/10.1504/ijvsmt.2009.029174.

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22

de Wit, Paulus A. J. M., and Roberto Moraes Cruz. "Learning from AF447: Human-machine interaction." Safety Science 112 (February 2019): 48–56. http://dx.doi.org/10.1016/j.ssci.2018.10.009.

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23

Roth, E. M., K. B. Bennett, and D. D. Woods. "Human interaction with an “intelligent” machine." International Journal of Man-Machine Studies 27, no. 5-6 (1987): 479–525. http://dx.doi.org/10.1016/s0020-7373(87)80012-3.

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24

Lu, Jia-ni, Hua Qian, Ai-ping Xiao, and Miao-wen Shi. "Human-machine Interaction Based on Voice." AASRI Procedia 3 (2012): 583–88. http://dx.doi.org/10.1016/j.aasri.2012.11.092.

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25

Dunne, B. J., and R. G. Jahn. "Experiments in Remote Human/Machine Interaction." EXPLORE 3, no. 3 (2007): 272. http://dx.doi.org/10.1016/j.explore.2007.03.025.

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26

Ma, Xue Liang, and Li Min Yu. "Study on the Feedback Information of Man-Machine Interface." Applied Mechanics and Materials 235 (November 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.235.340.

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This paper synthesizes the human-computer interaction and feedback from two aspects of the theory of in-depth research and analysis, reveals the interactive human-machine interfaces and inner relationship: human-computer interaction is a person and" contains the computer machines" effect relationship between scene depicts; and the human-machine interface is to achieve human-computer interaction forms and methods; at the same time, the system presents a new product development new thinking - interactive guide design. The design of the man-machine interface and real significance and related meth

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27

Lindvall, Martin, Jesper Molin, and Jonas Löwgren. "From machine learning to machine teaching." Interactions 25, no. 6 (2018): 52–57. http://dx.doi.org/10.1145/3282860.

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28

Sui, Zezhou, Mian Zhou, Zhikun Feng, Angelos Stefanidis, and Nan Jiang. "Language-Led Visual Grounding and Future Possibilities." Electronics 12, no. 14 (2023): 3142. http://dx.doi.org/10.3390/electronics12143142.

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In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language valida

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29

Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.

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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algori

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30

Xu, Jiandong, Jiong Pan, Tianrui Cui, Sheng Zhang, Yi Yang, and Tian-Ling Ren. "Recent Progress of Tactile and Force Sensors for Human–Machine Interaction." Sensors 23, no. 4 (2023): 1868. http://dx.doi.org/10.3390/s23041868.

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Human–Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human–machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoe

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31

Stephens, Keri, Anastazja Harris, Amanda Hughes, et al. "Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication." Human-Machine Communication 6 (July 1, 2023): 65–85. http://dx.doi.org/10.30658/hmc.6.5.

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Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perce

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32

Alonso-García, María, Ana García-Sánchez, Paula Jaén-Moreno, and Manuel Fernández-Rubio. "Performance Analysis of Urban Cleaning Devices Using Human–Machine Interaction Method." Sustainability 13, no. 11 (2021): 5846. http://dx.doi.org/10.3390/su13115846.

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Presently, several jobs require the collaboration of humans and machines to perform different services and tasks. The ease and intuitiveness of the worker when using each machine will not only improve the worker’s experience but also improve the company’s productivity and the satisfaction that all users have. Specifically, electromechanical devices used to provide cleaning services require complex interactions. These interactions determine the usability and performance of devices. Therefore, devices must have appropriate ergonomic arrangements for human–machine interactions. Otherwise, the des

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33

XUE Zhao-hong, 薛钊鸿, 孙梓瀚 SUN Zi-han, 成泽锐 CHENG Ze-rui, et al. "3D human-machine interaction based on human eye detection." Chinese Journal of Liquid Crystals and Displays 33, no. 11 (2018): 958–64. http://dx.doi.org/10.3788/yjyxs20183311.0958.

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34

WANG, Dangxiao, Yilei ZHENG, Teng LI, Cong PENG, Lijun WANG, and Yuru ZHANG. "Multi-modal human-machine interaction for human intelligence augmentation." SCIENTIA SINICA Informationis 48, no. 4 (2018): 449–65. http://dx.doi.org/10.1360/n112017-00213.

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35

Gambino, Andrew, Jesse Fox, and Rabindra Ratan. "Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm." Human-Machine Communication 1 (February 1, 2020): 71–86. http://dx.doi.org/10.30658/hmc.1.5.

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The computers are social actors framework (CASA), derived from the media equation, explains how people communicate with media and machines demonstrating social potential. Many studies have challenged CASA, yet it has not been revised. We argue that CASA needs to be expanded because people have changed, technologies have changed, and the way people interact with technologies has changed. We discuss the implications of these changes and propose an extension of CASA. Whereas CASA suggests humans mindlessly apply human-human social scripts to interactions with media agents, we argue that humans ma

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36

Yakura, Hiromu. "Failure-Resistant Intelligent Interaction for Reliable Human-AI Collaboration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16141–42. http://dx.doi.org/10.1609/aaai.v37i13.26931.

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My thesis is focusing on how we can overcome the gap people have against machine learning techniques that require a well-defined application scheme and can produce wrong results. I am planning to discuss the principle of the interaction design that fills such a gap based on my past projects that have explored better interactions for applying machine learning in various fields, such as malware analysis, executive coaching, photo editing, and so on. To this aim, my thesis also shed a light on the limitations of machine learning techniques, like adversarial examples, to highlight the importance o

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37

Siddique, AH, T. Shamsi, and M. Hasan. "Human Machine Interaction (HMI) in Offshore Drilling - oil rig workers’ opinion about their interaction with machines." International Journal of Occupational Safety and Health 11, no. 3 (2021): 181–91. http://dx.doi.org/10.3126/ijosh.v11i3.39812.

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Introduction: There are huge numbers of drilling platforms in the world and once the worker on those platforms meet with an accident, the situation could be very serious. The consequence of this could be environmental, economic and in some cases fatal. Middle East, being one of the oil rich regions hence some of the largest operator works here. Companies here own various types of jack up rigs ranging from old generation rigs to the latest cyber-rig. This paper addresses what oil rig workers have to say about their interaction with machines, and how Human Machine Interaction (HMI) in Offshore D

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38

Yang, Xi, Jichen Wang, Chong Gao, and Jiangpeng Hou. "Research on Human Machine Interaction of Exoskeleton." Advances in Engineering Technology Research 8, no. 1 (2023): 859. http://dx.doi.org/10.56028/aetr.8.1.859.2023.

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In the exoskeleton system, the interaction force between the exoskeleton and the human body is one of the important factors determining the efficiency of exoskeleton assistance, and the constraints generated by the positional deviation between the exoskeleton and the human body can affect the human-machine interaction force. Therefore, this article first focuses on the mechanical characteristics of the relative position deviation of the external skeleton binding point, and obtains the constraint characteristics in different directions through experiments. Secondly, different exoskeleton mechan

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39

Morimoto, Carlos Hitoshi, Flávio Coutinho, Jefferson Silva, Silvia Ghirotti, and Thiago Santos. "Computer Vision based Machine Interaction." Journal on Interactive Systems 2, no. 2 (2011): 1. http://dx.doi.org/10.5753/jis.2011.580.

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This paper introduces the Laboratory of Technologies for Interaction(LaTIn) and briefly describes its current main projects. The mainfocus of LaTInhas been developing new ways of human-machineinteraction using computer vision techniques. The projects arecathegorized according to the distance between the human user and themachine being operated. For close distances, appropriate forinteraction with desktop computers for example, we have developed eye-gazebased interfaces. We have also built hand and body gestures interfacesappropriate for kiosks and virtual reality settings and, for largedistanc

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40

Strutzenberger, Diana, Ronald Hinterbichler, Florian Pauker, and Thomas Frühwirth. "Information model for human-machine (tool) interaction." Procedia CIRP 99 (2021): 98–103. http://dx.doi.org/10.1016/j.procir.2021.03.016.

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41

Marache-Francisco, Cathie, and Eric Brangier. "Gamification and human-machine interaction: a synthesis." Le travail humain 78, no. 2 (2015): 165. http://dx.doi.org/10.3917/th.782.0165.

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42

Shan, Liuting, Yaqian Liu, Xianghong Zhang, et al. "Bioinspired kinesthetic system for human-machine interaction." Nano Energy 88 (October 2021): 106283. http://dx.doi.org/10.1016/j.nanoen.2021.106283.

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43

Lu, Lijun, Chunpeng Jiang, Guosheng Hu, Jingquan Liu, and Bin Yang. "Flexible Noncontact Sensing for Human–Machine Interaction." Advanced Materials 33, no. 16 (2021): 2100218. http://dx.doi.org/10.1002/adma.202100218.

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44

Casacuberta, Francisco, Jorge Civera, Elsa Cubel, et al. "Human interaction for high-quality machine translation." Communications of the ACM 52, no. 10 (2009): 135–38. http://dx.doi.org/10.1145/1562764.1562798.

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45

Heard, Jamison, Julian Fortune, and Julie A. Adams. "Speech Workload Estimation for Human-Machine Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 277–81. http://dx.doi.org/10.1177/1071181319631018.

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Performing tasks quickly and accurately in dynamic and intense environments is critical, such as supervising a remotely piloted aircraft; however, these environments contain periods of low and high workload, which can decrease task performance. A system capable of intelligently adapting its interaction modality based on the human’s workload state may mitigate these undesirable workload states: underload and overload. Such a system requires mechanisms to determine accurately the human’s overall workload state and each workload component state (i.e., cognitive, physical, visual, speech, and audi

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46

Nickerson, Raymond S. "New Methods for Modeling Human-Machine Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 4 (1996): 162–63. http://dx.doi.org/10.1177/154193129604000403.

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Use of the term mental model has proliferated in the discussion of human-machine interaction. Although it seems clear that humans must depend on mental models when doing problem solving in the domain of complex systems, the literature on the topic presents a confusing variety of perspectives, and there is little empirical evidence of the structure of the models people use or of how they influence human performance. The objectives of this symposium are to (a) provide a taxonomy for mental models and suggest a theory that is intended to unify what appear now to be disparate views, (b) outline an

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47

Lee, Hyun-Soon, Miao Xu, Naveen Senniappan Karuppusamy, and Bo-Yeong Kang. "Continuous Emotion Estimation for Human Machine Interaction." Advanced Science Letters 21, no. 3 (2015): 404–7. http://dx.doi.org/10.1166/asl.2015.5773.

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48

Moroni, Artemis, Fernando Von Zuben, and Jônatas Manzolli. "ArTbitration: Human-Machine Interaction in Artistic Domains." Leonardo 35, no. 2 (2002): 185–88. http://dx.doi.org/10.1162/00240940252940568.

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In this article, the authors analyze the process of humanmachine interaction in the context of artistic domains, as a framework for exploring creativity and producing results that could not be obtained without such interaction. “ArTbitration” denotes a process aimed at improving users' aesthetic judgment involving evolutionary computation and other computational intelligence methodologies. The authors interpret it as an interactive, iterative optimization process. They also suggest ArTbitration as an effective way to produce art through the efficient manipulation of information and the proper

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49

Barker, Philip. "The Many Faces of Human-Machine Interaction." British Journal of Educational Technology 17, no. 1 (1986): 74–80. http://dx.doi.org/10.1111/j.1467-8535.1986.tb00497.x.

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50

Schlick, C. M., C. Winkelholz, F. Motz, and H. Luczak. "Self-generated complexity and human-machine interaction." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 36, no. 1 (2006): 220–32. http://dx.doi.org/10.1109/tsmca.2005.859096.

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