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Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format
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Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format Example of IEEE Transactions on Human-Machine Systems format
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open access Open Access ISSN: 21682291
recommended Recommended

IEEE Transactions on Human-Machine Systems — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Human Factors and Ergonomics #1 of 39 up up by 1 rank
Computer Science Applications #53 of 693 up up by 25 ranks
Computer Networks and Communications #27 of 334 up up by 11 ranks
Control and Systems Engineering #22 of 260 up up by 11 ranks
Human-Computer Interaction #11 of 120 up up by 5 ranks
Artificial Intelligence #24 of 227 up up by 7 ranks
Signal Processing #13 of 108 up up by 3 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 289 Published Papers | 2737 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
Insights & related journals
General info
Top papers
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

3.374

1% from 2018

Impact factor for IEEE Transactions on Human-Machine Systems from 2016 - 2019
Year Value
2019 3.374
2018 3.332
2017 2.563
2016 2.493
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 1% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

9.5

6% from 2019

CiteRatio for IEEE Transactions on Human-Machine Systems from 2016 - 2020
Year Value
2020 9.5
2019 9.0
2018 7.3
2017 5.7
2016 5.4
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.873

26% from 2019

SJR for IEEE Transactions on Human-Machine Systems from 2016 - 2020
Year Value
2020 0.873
2019 1.185
2018 0.842
2017 0.603
2016 0.857
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 26% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

2.302

8% from 2019

SNIP for IEEE Transactions on Human-Machine Systems from 2016 - 2020
Year Value
2020 2.302
2019 2.512
2018 2.24
2017 2.239
2016 2.107
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 8% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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CiteRatio: 5.0 | SJR: 0.371 | SNIP: 1.169
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CiteRatio: 19.8 | SJR: 2.882 | SNIP: 3.86

IEEE Transactions on Human-Machine Systems

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IEEE

IEEE Transactions on Human-Machine Systems

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Transactions on Human-Machine Systems formatting guidelines as mentioned in IEEE author instructions. The current version was created on 22 Jul 2020 and has been used by 959 authors to write and format their manuscripts to this journal.

Human Factors and Ergonomics

Human-Computer Interaction

Computer Science Applications

Control and Systems Engineering

Computer Networks and Communications

Artificial Intelligence

Signal Processing

Social Sciences

i
Last updated on
22 Jul 2020
i
ISSN
2168-2291
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1109/THMS.2015.2470657
The GRASP Taxonomy of Human Grasp Types
Thomas Feix1, Javier Romero2, Heinz-Bodo Schmiedmayer3, Aaron M. Dollar1, Danica Kragic

Abstract:

In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different gras... In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human–computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size. read more read less

Topics:

GRASP (63%)63% related to the paper, Taxonomy (general) (53%)53% related to the paper
405 Citations
Journal Article DOI: 10.1109/TSMCC.2012.2215852
Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications
Giancarlo Fortino1, Roberta Giannantonio2, Raffaele Gravina1, Philip Kuryloski3, Roozbeh Jafari4

Abstract:

Wireless body sensor networks (BSNs) possess enormous potential for changing people's daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that l... Wireless body sensor networks (BSNs) possess enormous potential for changing people's daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed. read more read less

Topics:

Software framework (61%)61% related to the paper, Programming paradigm (60%)60% related to the paper, Middleware (56%)56% related to the paper, Programming complexity (55%)55% related to the paper, Wireless sensor network (55%)55% related to the paper
367 Citations
Journal Article DOI: 10.1109/THMS.2013.2293535
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues
Jessie Y. C. Chen1, Michael J. Barnes1

Abstract:

The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human d... The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human decision authority. A number of approaches-from flexible automation to autonomous agents-were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H-A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control. read more read less

Topics:

Situation awareness (53%)53% related to the paper, User interface (51%)51% related to the paper, Intelligent decision support system (51%)51% related to the paper
285 Citations
Journal Article DOI: 10.1109/TSMCC.2012.2219046
EEG-Based Brain-Controlled Mobile Robots: A Survey
Luzheng Bi1, Xin-an Fan1, Yili Liu2

Abstract:

EEG-based brain-controlled mobile robots can serve as powerful aids for severely disabled people in their daily life, especially to help them move voluntarily. In this paper, we provide a comprehensive review of the complete systems, key techniques, and evaluation issues of brain-controlled mobile robots along with some insig... EEG-based brain-controlled mobile robots can serve as powerful aids for severely disabled people in their daily life, especially to help them move voluntarily. In this paper, we provide a comprehensive review of the complete systems, key techniques, and evaluation issues of brain-controlled mobile robots along with some insights into related future research and development issues. We first review and classify various complete systems of brain-controlled mobile robots into two categories from the perspective of their operational modes. We then describe key techniques that are used in these brain-controlled mobile robots including the brain-computer interface techniques and shared control techniques. This description is followed by an analysis of the evaluation issues of brain-controlled mobile robots including participants, tasks and environments, and evaluation metrics. We conclude this paper with a discussion of the current challenges and future research directions. read more read less

Topics:

Mobile robot (60%)60% related to the paper
278 Citations
Journal Article DOI: 10.1109/THMS.2015.2504550
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks
Pichao Wang1, Wanqing Li1, Zhimin Gao1, Jing Zhang1, Chang Tang2, Philip Ogunbona1

Abstract:

This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative feature... This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the 3-D points of the captured depth maps. This not only synthesizes more data, but also makes the trained ConvNets view-tolerant. Second, WHDMMs at several temporal scales are constructed to encode the spatiotemporal motion patterns of actions into 2-D spatial structures. The 2-D spatial structures are further enhanced for recognition by converting the WHDMMs into pseudocolor images. Finally, the three ConvNets are initialized with the models obtained from ImageNet and fine-tuned independently on the color-coded WHDMMs constructed in three orthogonal planes. The proposed algorithm was evaluated on the MSRAction3D, MSRAction3DExt, UTKinect-Action, and MSRDailyActivity3D datasets using cross-subject protocols. In addition, the method was evaluated on the large dataset constructed from the above datasets. The proposed method achieved 2–9% better results on most of the individual datasets. Furthermore, the proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions. read more read less

Topics:

Convolutional neural network (55%)55% related to the paper, Artificial neural network (51%)51% related to the paper, Feature extraction (51%)51% related to the paper
257 Citations
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IEEE Transactions on Human-Machine Systems format uses IEEEtran citation style.

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Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the IEEE Transactions on Human-Machine Systems citation style.

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A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per IEEE Transactions on Human-Machine Systems's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

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To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
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SciSpace would allow download of your references in IEEE Transactions on Human-Machine Systems Endnote style, according to ieee guidelines.

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