Evidence map›Paper›PMID 37819889›Full record

ArticlePloS one2023

Interdisciplinary evaluation of a robot physically collaborating with workers.

Andrea Cherubini, Benjamin Navarro, Robin Passama, Sonny Tarbouriech, Shirley A Elprama, An Jacobs, Susanne Niehaus, Sascha Wischniewski, Freek J Tönis, Pim L Siahaya and 3 more

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Adaptive Lifting Index (Sensors (Basel, Switzerland) · 2024
    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

13 authors.

Andrea CherubiniLIRMM, Univ Montpellier, CNRS, Montpellier, France.ORCID 0000-0002-7272-6922
Benjamin NavarroLIRMM, Univ Montpellier, CNRS, Montpellier, France.
Robin PassamaLIRMM, Univ Montpellier, CNRS, Montpellier, France.
Sonny TarbouriechLIRMM, Univ Montpellier, CNRS, Montpellier, France.
Shirley A ElpramaIMEC-SMIT-Vrije Universiteit Brussel, Brussels, Belgium.
An JacobsIMEC-SMIT-Vrije Universiteit Brussel, Brussels, Belgium.
Susanne NiehausFederal Institute of Occupational Safety and Health, Dortmund, Germany.
Sascha WischniewskiFederal Institute of Occupational Safety and Health, Dortmund, Germany.
Freek J TönisHankamp Gears, Enschede, The Netherlands.
Pim L SiahayaHankamp Gears, Enschede, The Netherlands.
Giorgia ChiniINAIL, DiMEILA, Monte Porzio Catone (RM), Italy.ORCID 0000-0002-7654-0025
Tiwana VarrecchiaINAIL, DiMEILA, Monte Porzio Catone (RM), Italy.ORCID 0000-0003-3910-2728
Alberto RanavoloINAIL, DiMEILA, Monte Porzio Catone (RM), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collaborative Robots-CoBots-are emerging as a promising technological aid for workers. To date, most CoBots merely share their workspace or collaborate without contact, with their human partners. We claim that robots would be much more beneficial if they physically collaborated with the worker, on high payload tasks. To move high payloads, while remaining safe, the robot should use two or more lightweight arms. In this work, we address the following question: to what extent can robots help workers in physical human-robot collaboration tasks? To find an answer, we have gathered an interdisciplinary group, spanning from an industrial end user to cognitive ergonomists, and including biomechanicians and roboticists. We drew inspiration from an industrial process realized repetitively by workers of the SME HANKAMP (Netherlands). Eleven participants replicated the process, without and with the help of a robot. During the task, we monitored the participants' biomechanical activity. After the task, the participants completed a survey with usability and acceptability measures; seven workers of the SME completed the same survey. The results of our research are the following. First, by applying-for the first time in collaborative robotics-Potvin's method, we show that the robot substantially reduces the participants' muscular effort. Second: we design and present an unprecedented method for measuring the robot reliability and reproducibility in collaborative scenarios. Third: by correlating the worker's effort with the power measured by the robot, we show that the two agents act in energetic synergy. Fourth: the participant's increasing level of experience with robots shifts his/her focus from the robot's overall functionality towards finer expectations. Last but not least: workers and participants are willing to work with the robot and think it is useful.

Indexed as

RoboticsArmFemaleHumansMaleReproducibility of ResultsSurveys and QuestionnairesTechnology

Identifiers

PMID37819889
PMCPMC10566690

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.