Evidence map›Paper›PMID 33809721›Full record

ReviewSensors (Basel, Switzerland)2021

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges.

Kostas Nizamis, Alkinoos Athanasiou, Sofia Almpani, Christos Dimitrousis, Alexander Astaras

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
6.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 94 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Robotic rehabilitation with the LokomatFrontiers in aging neuroscience · 2026
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. [Progress in neurosurgical treatment of neurofibromatosis type 1].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2024
    Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Kostas NizamisDepartment of Design, Production and Management, University of Twente, 7522 NB Enschede, The Netherlands.ORCID 0000-0002-6965-0242
Alkinoos AthanasiouLab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0002-0269-4195
Sofia AlmpaniSchool of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece.ORCID 0000-0003-4823-479X
Christos DimitrousisDepartment of Computer Science, American College of Thessaloniki, 55535 Thessaloniki, Greece.
Alexander AstarasLab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0001-5583-7455
Anatolia College · GRAristotle University of Thessaloniki · GRNational Technical University of Athens · GRUniversity of Twente · NL

Funding

EASTERN COOPERATIVE ONCOLOGY GROUPU10CA016116 · NCI · JOHNS HOPKINS UNIVERSITY · PI FORASTIERE, ARLENE ANN · 1985 to 2013
$4.0M
NCI NIH HHS U10 CA016116
6 · The paper itself

Abstract

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Indexed as

RoboticsSpinal Cord InjuriesStrokeStroke RehabilitationArtificial IntelligenceElectromyographyHumansartificial intelligencebrain–computer interfacesexoskeletonhuman–robot interactionneural interfacesneurological disabilityneurorehabilitationrobotics

Identifiers

PMID33809721
PMCPMC8002299
OpenAlexW3136357392

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.