Evidence map›Paper›PMID 40756076›Full record

ArticleProduction & manufacturing research2025

A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations.

A Bonello, C A Brown, E Francalanza, M V Gauci, P Refalo

Abstract read
In one paragraph

Article in Production & manufacturing research, 2025. 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

5 authors.

A BonelloDepartment of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, Malta.ORCID https://orcid.org/0009-0006-8950-5268
C A BrownDepartment of Mechanical & Materials Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
E FrancalanzaDepartment of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, Malta.ORCID https://orcid.org/0000-0003-0317-5909
M V GauciDepartment of Disability Studies, Faculty for Social Wellbeing, University of Malta, Msida, Malta.
P RefaloDepartment of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, Malta.ORCID https://orcid.org/0000-0002-3252-0567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-centred design of collaborative human-robot (HRC) workspaces is central to Industry 5.0. While proximity with collaborative robots offers productivity and flexibility gains, it also raises concerns for both physical safety and cognitive ergonomics. Although physical safety is well addressed, few studies integrate cognitive and physical well-being into workstation design. This research presents a novel approach that combines Kansei Engineering (KE) with Suh's Axiomatic Design (AD) to support physically and cognitively safe HRC workstations. Unlike existing studies that rely solely on Suh's Axiom 1 (maintain independence), this work also takes into account Axiom 2 (minimise information) to select between equally independent physical and cognitive design parameters. The approach is demonstrated through a case-study workstation, visually illustrating the relationship between functional and physical metrics. This study advances the field by providing a novel replicable, human-centred approach that unites cognitive and physical ergonomics,bridging theory and practical application for both academic and industrial contexts.

Indexed as

Axiomatic designcognitivecollaborative workstationKansei Engineeringquantification

Identifiers

PMID40756076
PMCPMC12315848

What OpenQuestion holds

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LicenceCC BY
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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.