ArticleAnnals of biomedical engineering2018
A Semi-passive Planar Manipulandum for Upper-Extremity Rehabilitation.
Article in Annals of biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 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
7 citing papers in PubMed, 17 citations in OpenAlex.
- Low-cost Devices for Stroke Rehabilitation: A Review of Approaches, Designs, and Evidence.Restorative neurology and neuroscience · 2026Review
- Manipulating abnormal synergistic coupling of joint torques through force applications at the Hand: A Simulation-Based study.Journal of biomechanics · 2022Article
- Motor Modules are Impacted by the Number of Reaching Directions Included in the Analysis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2020Article
- A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training.IEEE transactions on bio-medical engineering · 2019Article
- Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training.Sensors (Basel, Switzerland) · 2018Article
- Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study.Restorative neurology and neuroscience · 2018Article
- Teaching Motor Skills Without a Motor: A Semi-Passive Robot to Facilitate Learning.IEEE transactions on hapticsArticle
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Robotic rehabilitation is a promising approach to treat individuals with neurological or orthopedic disorders. However, despite significant advancements in the field of rehabilitation robotics, this technology has found limited traction in clinical practice. A key reason for this issue is that most robots are expensive, bulky, and not scalable for in-home rehabilitation. Here, we introduce a semi-passive rehabilitation robot (SepaRRo) that uses controllable passive actuators (i.e., brakes) to provide controllable resistances at the end-effector over a large workspace in a manner that is cost-effective and safe for in-home use. We also validated the device through theoretical analyses, hardware experiments, and human subject experiments. We found that by including kinematic redundancies in the robot's linkages, the device was able to provide controllable resistances to purely resist the movement of the end-effector, or to gently steer (i.e., perturb) its motion away from the intended path. When testing these capabilities on human subjects, we found that many of the upper-extremity muscles could be selectively targeted based on the forcefield prescribed to the user. These results indicate that SepaRRo could serve as a low-cost therapeutic tool for upper-extremity rehabilitation; however, further testing is required to evaluate its therapeutic benefits in patient population.
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.