Evidence map›Paper›PMID 40817149›Full record

ArticleScientific reports2025

Tuning of task-relevant stiffness in multiple directions.

Chenguang Zhang, Federico Tessari, James Hermus, Himanshu Akolkar, Neville Hogan, Andrew B Schwartz

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Chenguang ZhangDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Federico TessariDepartment of Mechanical Engineering, MIT, Cambridge, MA, USA.
James HermusDepartment of Mechanical Engineering, MIT, Cambridge, MA, USA.
Himanshu AkolkarDepartment of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
Neville HoganRobot Learning & Interaction Group, Idiap Research Institute, Martigny, Switzerland.
Andrew B SchwartzDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. abs21@pitt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In contrast to robots, humans can rapidly and elegantly modulate the impedance of their arms and hands during initial contact with objects. Anticipating collisions by setting mechanical impedance to counter near-instantaneous changes in force and displacement is one reason we excel at manipulating objects. We investigated the ability to set impedance in an object interaction task with rapid changes in force and displacement, like those encountered during manipulation in different directions. Subjects (n = 20) predictively co-activated antagonist muscles to adjust one component of the impedance - stiffness - to match the task demands before the movement began, irrespective of movement direction. Subjects adopted the minimal stiffness needed to complete the task, but when pushed to the most difficult condition, they were limited by their ability to produce high stiffness rather than large force. This robust and simple strategy ensured task success at the expense of energy efficiency. Our results confirm the ability of humans to predictively set and control mechanical impedance in task-relevant directions in anticipation of breaking contact. This offers the prospect that future investigations will find neural correlates of impedance, which in turn, could improve the ability of neuro-prosthetic limbs to interact with objects.

Indexed as

Muscle, SkeletalPsychomotor PerformanceAdultArmBiomechanical PhenomenaFemaleHandHumansMaleMovementTask Performance and AnalysisYoung Adult

Identifiers

PMID40817149
PMCPMC12356977

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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.