Evidence map›Paper›PMID 39226317›Full record

ArticlePLoS computational biology2024

Finding the rhythm: Humans exploit nonlinear intrinsic dynamics of compliant systems in periodic interaction tasks.

Annika Schmidt, Marion Forano, Arne Sachtler, Davide Calzolari, Bernhard M Weber, David W Franklin, Alin Albu-Schäffer

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Annika SchmidtSensor Based Robotic Systems and Intelligent Assistance Systems, TUM School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.ORCID 0000-0002-4718-4201
Marion ForanoMunich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Munich, Germany.ORCID 0000-0002-6878-1427
Arne SachtlerSensor Based Robotic Systems and Intelligent Assistance Systems, TUM School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.ORCID 0000-0003-4974-4134
Davide CalzolariSensor Based Robotic Systems and Intelligent Assistance Systems, TUM School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.
Bernhard M WeberInstitute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany.
David W FranklinMunich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Munich, Germany.
Alin Albu-SchäfferSensor Based Robotic Systems and Intelligent Assistance Systems, TUM School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activities like ball bouncing and trampoline jumping showcase the human ability to intuitively tune to system dynamics and excite motions that the system prefers intrinsically. This human sensitivity to resonance has been experimentally supported for interactions with simple linear systems but remains a challenge to validate in more complex scenarios where nonlinear dynamics cannot be predicted analytically. However, it has been found that many nonlinear systems exhibit periodic orbits similar to the eigenmodes of linear systems. These nonlinear normal modes (NNM) are computable with a recently developed numerical mode tool. Using this tool, the present resarch compared the motions that humans excite in nonlinear systems with the predicted NNM of the energy-conservative systems. In a user study consisting of three experiment parts, participants commanded differently configured virtual double pendula with joint compliance through a haptic joystick. The task was to alternately hit two targets, which were either aligned with the NNM (Experiments 1 and 2) or purposefully arranged offset (Experiment 3). In all tested experiment variations, participants intuitively applied a control strategy that excited the resonance and stabilized an orbit close to the ideal NNM of the conservative systems. Even for increased task accuracy (Experiment 2) and targets located away from the NNM (Experiment 3), participants could successfully accomplish the task, likely by adjusting their arm stiffness to alter the system dynamics to better align the resonant motions to the task. Consequently, our experiments extend the existing research on human resonance sensitivity with data-based evidence to nonlinear systems. Our findings emphasize the human capabilities to apply control strategies to excite and exploit resonant motions in dynamic object interactions, including possibly shaping the dynamics through changes in muscle stiffness.

Indexed as

Nonlinear DynamicsAdultComputational BiologyFemaleHumansMaleMovementYoung Adult

Identifiers

PMID39226317
PMCPMC11398697

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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.