Evidence map›Paper›PMID 42022478›Full record

ArticleIEEE transactions on medical robotics and bionics2026

Autonomous Slip-Prevention Grip Force Control and Its Potential in Shared Control of Robotic Prosthetic Hands.

Joseph Berman, Varun Nalam, Jie Yin, He Huang

Abstract read
In one paragraph

Article in IEEE transactions on medical robotics and bionics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Joseph BermanDepartment of Electrical and Computer Engineering, North Carolina State University, USA.
Varun NalamNCSU/UNC Department of Biomedical Engineering, NC State University, Raleigh, NC, 27695-7115; University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.
Jie YinDepartment of Mechanical and Aerospace Engineering, North Carolina State University, USA.
He HuangNCSU/UNC Department of Biomedical Engineering, NC State University, Raleigh, NC, 27695-7115; University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.

Funding

Development of a Novel EMG-Based Neural Interface for Control of Transradial Prostheses with Gripping AssistanceF31HD111290 · NICHD · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI BERMAN, JOSEPH · 2023 to 2023
$31k
NICHD NIH HHS F31 HD111290
6 · The paper itself

Abstract

This study aimed to develop and validate a novel autonomous slip-prevention grip force controller for a robotic prosthetic hand. The controller utilized an optical flow sensor to detect object slippage events, which triggered increases in the desired gripping force within a closed-loop force control system. We first validated the slip-prevention functionality on a bench test platform. Subsequently, the controller was integrated into a simple shared control framework to demonstrate its feasibility in enhancing the grasping function for prosthesis users. In this shared control framework, myoelectric position control (i.e., user control) was employed when the hand was not engaged in a grasp, while the intelligent autonomous grip force control was activated during contact with an object. The feasibility of the shared control was tested with human participants performing tasks involving the grasping, transferring, and releasing of various objects while using and wearing the prosthetic hand. Additionally, we compared the shared control to a myoelectric-only controller (without the autonomous grip force control) in the object manipulation task combined with a secondary cognitive task. The results demonstrated that the proposed automatic grip force controller effectively prevented slips and improved grasping stability under various perturbations. Compared to myoelectric-only control, the shared control reduced the user's cognitive effort required for object manipulation and lead to increased task success. While our designed slip-prevention grip force controller shows significant potential to assist the prosthesis users in improving grasping functionality, further exploration of other shared control paradigms is necessary to maximize user performance and acceptance in the future.

Indexed as

electromyographyforce controlgraspingoptical sensorsProsthesesslip prevention control

Identifiers

PMID42022478
PMCPMC13099084

What OpenQuestion holds

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Registered trials

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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.