ReviewJournal of neuroengineering and rehabilitation2017
Robot-assisted assessment of muscle strength.
Review in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques.Sensors (Basel, Switzerland) · 2021Pooled it
- Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Rehabilitation training robot using mirror therapy for the upper and lower limb after stroke: a prospective cohort study.Journal of neuroengineering and rehabilitation · 2025Article
- SpasticSim: a synthetic data generation method for upper limb spasticity modelling in neurorehabilitation.Scientific reports · 2024Article
- Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment.Research (Washington, D.C.) · 2024Article
- Robot-Aided Motion Analysis in Neurorehabilitation: Benefits and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Impairment of neuromuscular function in neurological disorders leads to reductions in muscle force, which may lower quality of life. Rehabilitation robots that are equipped with sensors are able to quantify the extent of muscle force impairment and to monitor a patient during the process of neurorehabilitation with sensitive and objective assessment methods. In this article, we provide an overview of fundamental aspects of muscle function and how the corresponding variables can be quantified by means of meaningful robotic assessments that are primarily oriented towards upper limb neurorehabilitation. We discuss new concepts for the assessment of muscle function, and present an overview of the currently available systems for upper limb measurements. These considerations culminate in practical recommendations and caveats for the rational quantification of force magnitude, force direction, moment of a force, impulse, critical force (neuromuscular fatigue threshold) and state and trait levels of fatigue.
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Identifiers
What OpenQuestion holds
Registered trials
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.