IEEE Transactions on Human-Machine Systems

Top Research Topics at IEEE Transactions on Human-Machine Systems?

IEEE Transactions on Human-Machine Systems is organized to address concerns in the fields of Artificial intelligence, Human–computer interaction, Computer vision, Simulation and Task analysis. The research on Artificial intelligence featured in IEEE Transactions on Human-Machine Systems combines topics in other fields like Machine learning and Pattern recognition. IEEE Transactions on Human-Machine Systems explores topics in Human–computer interaction which can be helpful for research in disciplines like Visualization, Interface (computing), User interface and Gesture.

The journal links adjacent topics like Computer vision with Wearable computer. Haptic technology is the primary subject of Simulation works presented in the journal. The studies on Task analysis discussed can also contribute to research in the domains of Automation and Workload.

Human–robot interaction, Social robot and Robot control are all topics related to Robot research discussed. Most of the works presented in IEEE Transactions on Human-Machine Systems deals with Feature extraction but it intersects with the subject of Speech recognition. Discussions in it are anchored in the subject of Speech recognition and the similar topic of Handwriting.

• Artificial intelligence (36.57%)
• Human–computer interaction (20.61%)
• Computer vision (17.27%)

What are the most cited papers published in the journal?

• The GRASP Taxonomy of Human Grasp Types (349 citations)
• Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications (329 citations)
• Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (229 citations)

Research areas of the most cited articles at IEEE Transactions on Human-Machine Systems:

The journal papers primarily focus on research topics in Artificial intelligence, Computer vision, Human–computer interaction, Feature extraction and Pattern recognition. While the most cited papers focused on Artificial intelligence, they were also able to explore topics like Machine learning and Speech recognition. Aside from discussions in Human–computer interaction, the published articles also deal with the subject of Automation which intersects with Event (computing) and User interface disciplines.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Operating system
• Machine learning

The previous edition focused in particular on these issues:

The main points discussed in the journal deals with Artificial intelligence, Task analysis, Human–computer interaction, Task (project management) and Computer vision. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Machine learning. The Machine learning works featured in IEEE Transactions on Human-Machine Systems incorporate elements from Data modeling and Robot.

The subject of Workload, which is connected to the field of Eye tracking, serves as the foundation of the Task analysis research featured in it. Gesture, Assistive robot and Haptic technology are some topics wherein Human–computer interaction research discussed in it have an impact. Topics in Gesture recognition were tackled in line with various other fields like Radar and Modality (human–computer interaction).

The most cited articles from the last journal are:

• A Self-Paced BCI With a Collaborative Controller for Highly Reliable Wheelchair Driving: Experimental Tests With Physically Disabled Individuals (4 citations)
• Holoscopic 3D Microgesture Recognition by Deep Neural Network Model Based on Viewpoint Images and Decision Fusion (2 citations)
• Gesture Recognition Using Reflected Visible and Infrared Lightwave Signals (2 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Transactions on Human-Machine Systems (based on the number of publications) are:

• Max Mulder (18 papers) absent at the last edition,
• Marinus M. van Paassen (13 papers) absent at the last edition,
• Ellen J. Bass (8 papers) absent at the last edition,
• Bin Guo (7 papers) published 1 paper at the last edition,
• David A. Abbink (7 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Transactions on Human-Machine Systems (based on the number of publications) are:

• Delft University of Technology (29 papers) absent at the last edition,
• Georgia Institute of Technology (16 papers) absent at the last edition,
• Texas A&M University (12 papers) published 3 papers at the last edition, 2 more than at the previous edition,
• Drexel University (11 papers) published 1 paper at the last edition,
• University of Central Florida (11 papers) published 1 paper at the last edition, 2 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 32.43% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.00% of all publications and 56.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Applying to Academic Research: How to Become a Contributor

Understanding the research scope and dominant topics in IEEE Transactions on Human-Machine Systems are foundational steps in becoming a contributor. But equally important is recognizing the specific occupational demands connected to this line of work like obtaining relevant qualifications and experience. As a potential contributor, you may wonder how to get started in an academic research career and the required educational background and skills. In particular, securing an advanced degree such as a master's can provide comprehensive training and skills that support impactful research contributions. For example, let's consider the teaching profession - transitioning from a teacher to an academic researcher is a possible career pathway. However, this requires understanding the prerequisites and extensively preparing for it. For anyone considering this shift, it can be invaluable to understand the process of obtaining necessary qualifications. This is similar to the journey a teacher in Washington might undertake to get their master’s degree. The pathway outlines the coursework needed, state certification process, and necessary student teaching hours. Similar to this,{how to become a teacher in washington with a master's degree}, having a specific career pathway to an academic research career can guide aspiring researchers to align their skills and experience effectively. By offering insights into both the subject matter and the professional qualifications involved in academic research in Human-Machine Systems, potential contributors can gain a solid footing and embark on a successful career trajectory. Professional growth in this field starts with understanding its core, exploring the connections to related professions, and delving into the educational aspirations tied to effective research contributions.