Evidence map›Paper›PMID 36772209›Full record

ArticleSensors (Basel, Switzerland)2023

HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics.

Karmele Lopez-de-Ipina, Jon Iradi, Elsa Fernandez, Pilar M Calvo, Damien Salle, Anujan Poologaindran, Ivan Villaverde, Paul Daelman, Emilio Sanchez, Catalina Requejo and 1 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Karmele Lopez-de-IpinaDepartment of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK.ORCID 0000-0002-2135-2393
Jon IradiEleKin Lab, Systems Engineering and Automation, Computers' Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain.
Elsa FernandezEleKin Lab, Systems Engineering and Automation, Computers' Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain.
Pilar M CalvoEleKin Lab, Systems Engineering and Automation, Computers' Architecture and Technology, and Enterprise Management Departments, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastian, Spain.
Damien SalleTecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain.
Anujan PoologaindranDepartment of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK.
Ivan VillaverdeTecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain.ORCID 0000-0001-6119-8987
Paul DaelmanTecnalia Research Centre, Tecnalia Industry and Transport Division, 20009 Donostia-San Sebastia, Spain.
Emilio SanchezDepartment of Mechanical Engineering and Materials, Engineering School, University of Navarra, TECNUN, 20018 Donostia-San Sebastian, Spain.ORCID 0000-0001-9387-9437
Catalina RequejoCajal Institute, Consejo Superior de Investigaciones Científicas (CSIC), 28002 Madrid, Spain.
John SucklingDepartment of Psychiatry, University of Cambridge, Cambridge CB2 3PT, UK.ORCID 0000-0002-5098-1527

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers' support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers' well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker's models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers' health information towards a successful risk management strategy for safe industrial Cobot environments.

Indexed as

Mental DisordersOccupational HealthHealth StatusHumansWorkplaceageing populationCobothuman/robot behaviourindustrial health and safetyMachine Learningrisk managementworkers’ diseases

Identifiers

PMID36772209
PMCPMC9920065

What OpenQuestion holds

Textmetadata
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.