Example of Journal of NeuroEngineering and Rehabilitation format
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Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format
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Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format Example of Journal of NeuroEngineering and Rehabilitation format
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open access Open Access
recommended Recommended

Journal of NeuroEngineering and Rehabilitation — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Rehabilitation #3 of 118 down down by 1 rank
Health Informatics #14 of 95 down down by 11 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 553 Published Papers | 3661 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/06/2020
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Related Journals

open access Open Access
recommended Recommended

SAGE

Quality:  
High
CiteRatio: 7.9
SJR: 1.192
SNIP: 2.105
open access Open Access
recommended Recommended

SAGE

Quality:  
High
CiteRatio: 6.8
SJR: 1.651
SNIP: 1.671
open access Open Access
recommended Recommended

SAGE

Quality:  
High
CiteRatio: 4.9
SJR: 1.15
SNIP: 1.696
open access Open Access
recommended Recommended

SAGE

Quality:  
High
CiteRatio: 4.3
SJR: 0.603
SNIP: 1.079

Journal Performance & Insights

Impact Factor

CiteRatio

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.

A measure of average citations received per peer-reviewed paper published in the journal.

3.519

2% from 2018

Impact factor for Journal of NeuroEngineering and Rehabilitation from 2016 - 2019
Year Value
2019 3.519
2018 3.582
2017 3.865
2016 3.516
graph view Graph view
table view Table view

6.6

2% from 2019

CiteRatio for Journal of NeuroEngineering and Rehabilitation from 2016 - 2020
Year Value
2020 6.6
2019 6.5
2018 7.0
2017 8.2
2016 6.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 2% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 2% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.094

4% from 2019

SJR for Journal of NeuroEngineering and Rehabilitation from 2016 - 2020
Year Value
2020 1.094
2019 1.139
2018 1.298
2017 1.515
2016 1.189
graph view Graph view
table view Table view

1.726

5% from 2019

SNIP for Journal of NeuroEngineering and Rehabilitation from 2016 - 2020
Year Value
2020 1.726
2019 1.815
2018 1.9
2017 2.018
2016 1.897
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 4% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 5% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of NeuroEngineering and Rehabilitation

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Springer

Journal of NeuroEngineering and Rehabilitation

Approved by publishing and review experts on SciSpace, this template is built as per for Journal of NeuroEngineering and Rehabilitation formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 922 authors to write and format their manuscripts to this journal.

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Last updated on
15 Jun 2020
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ISSN
1606-8610
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25(7), 4515–4532 (1982)

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1743-0003-9-21
A review of wearable sensors and systems with application in rehabilitation.

Abstract:

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community setti... The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed. read more read less

Topics:

Wearable technology (67%)67% related to the paper, Wearable computer (61%)61% related to the paper
View PDF
1,826 Citations
open accessOpen access Journal Article DOI: 10.1186/1743-0003-2-6
A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation
Emil Jovanov1, Aleksandar Milenkovic1, Chris A. Otto1, Piet C. de Groen2

Abstract:

Background Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. Background Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. read more read less

Topics:

Body area network (69%)69% related to the paper, Wi-Fi array (62%)62% related to the paper, Wireless (51%)51% related to the paper
View PDF
1,114 Citations
open accessOpen access Journal Article DOI: 10.1186/1743-0003-5-25
Review on solving the inverse problem in EEG source analysis

Abstract:

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performan... In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions. read more read less

Topics:

Inverse problem (52%)52% related to the paper
View PDF
1,013 Citations
open accessOpen access Journal Article DOI: 10.1186/1743-0003-6-20
Review of control strategies for robotic movement training after neurologic injury

Abstract:

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulat... There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies. read more read less
View PDF
992 Citations
open accessOpen access Journal Article DOI: 10.1186/1743-0003-12-1
Control strategies for active lower extremity prosthetics and orthotics: a review

Abstract:

Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them... Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user. This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies. As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os – not as independent devices, but as actors within an ecosystem – is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers. Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use. The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL. read more read less

Topics:

Orthotic device (56%)56% related to the paper, Control theory (51%)51% related to the paper, Control system (51%)51% related to the paper, Rehabilitation robotics (50%)50% related to the paper
View PDF
853 Citations
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Frequently asked questions

1. Can I write Journal of NeuroEngineering and Rehabilitation in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of NeuroEngineering and Rehabilitation guidelines and auto format it.

2. Do you follow the Journal of NeuroEngineering and Rehabilitation guidelines?

Yes, the template is compliant with the Journal of NeuroEngineering and Rehabilitation guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of NeuroEngineering and Rehabilitation?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of NeuroEngineering and Rehabilitation citation style.

4. Can I use the Journal of NeuroEngineering and Rehabilitation templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of NeuroEngineering and Rehabilitation.

5. Can I use a manuscript in Journal of NeuroEngineering and Rehabilitation that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of NeuroEngineering and Rehabilitation that you can download at the end.

6. How long does it usually take you to format my papers in Journal of NeuroEngineering and Rehabilitation?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Journal of NeuroEngineering and Rehabilitation.

7. Where can I find the template for the Journal of NeuroEngineering and Rehabilitation?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of NeuroEngineering and Rehabilitation's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of NeuroEngineering and Rehabilitation's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Journal of NeuroEngineering and Rehabilitation an online tool or is there a desktop version?

SciSpace's Journal of NeuroEngineering and Rehabilitation is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of NeuroEngineering and Rehabilitation?

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After writing your paper autoformatting in Journal of NeuroEngineering and Rehabilitation, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of NeuroEngineering and Rehabilitation's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of NeuroEngineering and Rehabilitation?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of NeuroEngineering and Rehabilitation. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In Journal of NeuroEngineering and Rehabilitation?

The 5 most common citation types in order of usage for Journal of NeuroEngineering and Rehabilitation are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of NeuroEngineering and Rehabilitation?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of NeuroEngineering and Rehabilitation's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Journal of NeuroEngineering and Rehabilitation in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of NeuroEngineering and Rehabilitation Endnote style according to Elsevier guidelines.

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