ArticleSensors (Basel, Switzerland)2025
Reducing Upper-Limb Muscle Effort with Model-Based Gravity Compensation During Robot-Assisted Movement.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Intelligent Robot-Aided, Task-Oriented Physiotherapy Method for Upper Limb Rehabilitation: Development and Usability Study.JMIR rehabilitation and assistive technologies · 2026Article
- Robot-Assisted Gravity Compensation for Upper Limb Motor Rehabilitation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
- Comparative Analysis of Passive Movement During Robot-Assisted and Therapist-Led Rehabilitation Exercises.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Clinical research has demonstrated that stroke patients benefit from active participation during robot-assisted training. However, the weight of the arm impedes the execution of tasks and movements due to the functional disability. The purpose of this paper is to develop a gravity compensation strategy for an end-effector upper limb rehabilitation robot based on an arm dynamics model to reduce the arm's muscle activation level. This control strategy enables real-time evaluation of arm gravity torques based on feedback from upper limb kinematic parameters. The performance of the proposed strategy in movement tracking is then compared to that of other types of weight compensation strategies. Experimental results demonstrate that compared to movements without compensation, the mean activation levels of arm muscles with the proposed strategy showed a significant reduction (
Indexed as
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.