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Sensors
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Human-Machine Intelligence Hybridization: Challenges, Approaches, Applications
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Human-Machine Intelligence Hybridization: Challenges, Approaches, Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 6980

Special Issue Editors

Dr. Kirill Krinkin

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Guest Editor
Faculty of Computer Science and Information Technologies, Saint-Petersburg Electrotechnical University “LETI”, 197376 Saint-Petersburg, Russia
Interests: operating systems and virtualization; autonomous mobile robots; SLAM algorithms; network technologies and protocols; distributed systems; next-generation education methods; software architecture for next generation industry, co-evolutionary hybrid Intelligence
Prof. Dr. Antonio Fernández-Caballero

E-Mail Website
Guest Editor
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: pattern recognition; human–computer interaction; affective computing; computer vision; multi-sensor fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wai Lok Woo

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is one of the drivers of modern technological development. Today, challenges are related to the complexity of human-created systems (social, technical, economic, political, etc.) and natural processes (or consequences of human actions in the biosphere, such as pandemics or climate change).

The current approach to the development of intelligent systems is mostly data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; and solutions are not explainable. Modern AI systems (based on narrow AI) can hardly be considered intelligence. It is rather the next level of automation of human work.

A promising concept that is devoid of the above limitations is the concept of hybrid intelligence, integrating the strengths of narrow AI and human capabilities. Hybrid intelligent systems have the following key features:

Cognitive interoperability—allows artificial and natural intelligent agents to easily communicate to solve a problem together;

Mutual evolution (co-evolution)—allows a hybrid system to develop, accumulate knowledge, and form a common ontology of the subject area.

This Special Issue intends to collect and systematize modern challenges, solutions, and applications of hybrid evolving intelligent systems.

  • The core of human–machine intelligence hybridization is interoperability bio and technical systems on different levels from physical signals to cognitive models. Sensors fits such a comprehensive interface, providing a platform for interdisciplinary scientific discussion.

Dr. Kirill Krinkin
Prof. Dr. Antonio Fernández-Caballero
Prof. Dr. Wai Lok Woo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

13 pages, 4432 KiB  
Article
Strain Gauge Measuring System for Subsensory Micromotions Analysis as an Element of a Hybrid Human–Machine Interface
by Olga Bureneva and Nikolay Safyannikov
Sensors 2022, 22(23), 9146; https://doi.org/10.3390/s22239146 - 25 Nov 2022
Cited by 2 | Viewed by 1474
Abstract
The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis [...] Read more.
The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis of one set of integrative system components makes it possible to draw conclusions about the state and efficiency of the other components. Thus, to evaluate a person’s cognitive properties, we can assess their involuntary motor acts, i.e., a person’s subsensory reactions. To measure the parameters of involuntary motor acts, we have developed a strain gauge measuring system. This system provides measurement and estimation of the parameters of involuntary movements against the background of voluntary isometric efforts. The article presents the architecture of the system and shows the organization of the primary signal processing in analog form, in particular the separation of the signal taken from the strain-gauge sensor into frequency and smoothly varying components by averaging and subtracting the analog signals. This transfer to analog form simplifies the implementation of the digital part of the measuring system and allowed for minimizing the response time of the system while displaying the isometric forces in the visual feedback channel. The article describes the realization of the system elements and shows the results of its experimental research. Full article
(This article belongs to the Special Issue Human-Machine Intelligence Hybridization: Challenges, Approaches, Applications)
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13 pages, 3726 KiB  
Article
Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine Intelligence
by Chadia Ed-Driouch, Franck Mars, Pierre-Antoine Gourraud and Cédric Dumas
Sensors 2022, 22(21), 8313; https://doi.org/10.3390/s22218313 - 29 Oct 2022
Cited by 5 | Viewed by 1931
Abstract
Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions [...] Read more.
Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human–machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians’ knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician–algorithm method showed that the models based on the latter can perform better. Physicians’ engagement is the most promising condition for the safe and innovative use of ML in healthcare. Full article
(This article belongs to the Special Issue Human-Machine Intelligence Hybridization: Challenges, Approaches, Applications)
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11 pages, 1124 KiB  
Article
Towards Dynamic Model-Based Agile Architecting of Cyber-Physical Systems
by Alexander Vodyaho, Nataly Zhukova, Alexey Subbotin and Fahem Anaam
Sensors 2022, 22(8), 3078; https://doi.org/10.3390/s22083078 - 17 Apr 2022
Cited by 4 | Viewed by 2187
Abstract
A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also [...] Read more.
A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also be considered as one of the possible realizations of the digital thread paradigm. As part of this approach, a three-level digital thread reference architecture is suggested, which includes the following levels: (i) digital thread support level; (ii) agile architecture support level; (iii) digital shadow support level. This approach has been used in the development of a number of real systems, and has shown its effectiveness in supporting system agility at the exploitation and modernization stages. The proposed approach is focused on building digital twin-based systems. This article may be interesting for specialists engaged in research and development in the domain of IoT- and IIoT-based information systems, primarily architects. Full article
(This article belongs to the Special Issue Human-Machine Intelligence Hybridization: Challenges, Approaches, Applications)
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