ArticleSensors (Basel, Switzerland)2024
Human-in-the-Loop Modeling and Bilateral Skill Transfer Control of Soft Exoskeleton.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Pathology-Informed Personalized Exoskeleton Assistance for Post-Stroke Gait Rehabilitation via Simulation-to-Real Reinforcement Learning.Healthcare (Basel, Switzerland) · 2026Article
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
Abstract
Soft exoskeletons (exosuits) are expected to provide a comfortable wearing experience and compliant assistance compared with traditional rigid exoskeleton robots. In this paper, an exosuit with twisted string actuators (TSAs) is developed to provide high-strength and variable-stiffness actuation for hemiplegic patients. By formulating the analytic model of the TSA and decoding the human impedance characteristic, the human-exosuit coupled dynamic model is constructed. An adaptive impedance controller is designed to transfer the skills of the patient's healthy limb (HL) to the bilateral impaired limb (IL) with a mirror training strategy, including the movement trajectory and stiffness profiles. A reinforcement learning (RL) algorithm is proposed to optimize the robotic assistance by adapting the impedance model parameters to the subject's performance. Experiments are conducted to demonstrate the effectiveness and superiority of the proposed method.
Indexed as
Identifiers
What Socratic 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 Socratic graph.