Evidence map›Paper›PMID 34919539›Full record

ArticlePLoS computational biology2021

Preparing to move: Setting initial conditions to simplify interactions with complex objects.

Rashida Nayeem, Salah Bazzi, Mohsen Sadeghi, Neville Hogan, Dagmar Sternad

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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

15 citing papers in PubMed, 28 citations in OpenAlex.

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  12. Novel Platform for Quantitative Assessment of Functional Object Interactions After Stroke.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023
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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

5 authors at 2 institutions in 1 country.

Rashida NayeemDepartment of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America.ORCID 0000-0001-6749-5077
Salah BazziDepartment of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America.ORCID 0000-0002-8631-0426
Mohsen SadeghiDepartment of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America.ORCID 0000-0003-2573-146X
Neville HoganDepartments of Mechanical Engineering and Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0001-5366-2145
Dagmar SternadDepartment of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America.ORCID 0000-0001-9318-2920
Northeastern University · USMassachusetts Institute of Technology · US

Funding

CRCNS Research Proposal: Collaborative Research: Neural Basis of Motor ExpertiseR01NS120579 · NINDS · NORTHEASTERN UNIVERSITY · PI BATISTA, AARON PAUL, STERNAD, DAGMAR · 2020 to 2025
$2.0M
Predictability in Complex Object ControlR01HD087089 · NICHD · NORTHEASTERN UNIVERSITY · PI STERNAD, DAGMAR · 2015 to 2020
$1.8M
NICHD NIH HHS R01 HD087089NINDS NIH HHS R01 NS120579
6 · The paper itself

Abstract

Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object's dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of 'carrying a cup of coffee', a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects' strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects' behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.

Indexed as

AdultBiomechanical PhenomenaComputer SimulationFemaleHandHumansMaleMotor SkillsMovementVirtual RealityYoung Adult

Identifiers

PMID34919539
PMCPMC8683040
OpenAlexW4200401280

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.