Evidence map›Paper›PMID 35161503›Full record

ArticleSensors (Basel, Switzerland)2022

My Caregiver the Cobot: Comparing Visualization Techniques to Effectively Communicate Cobot Perception to People with Physical Impairments.

Max Pascher, Kirill Kronhardt, Til Franzen, Uwe Gruenefeld, Stefan Schneegass, Jens Gerken

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 20% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
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

6 authors at 2 institutions in 1 country.

Max PascherHuman-Computer Interaction Group, Department of Media Informatics and Communication, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany.ORCID 0000-0002-6847-0696
Kirill KronhardtHuman-Computer Interaction Group, Department of Media Informatics and Communication, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany.ORCID 0000-0002-0460-3787
Til FranzenHuman-Computer Interaction Group, Department of Media Informatics and Communication, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany.ORCID 0000-0003-0203-7512
Uwe GruenefeldHuman-Computer Interaction Group, Paluno-The Ruhr Institute for Software Technology, Faculty of Business Administration and Economics, University of Duisburg-Essen, 45127 Essen, Germany.ORCID 0000-0002-5671-1640
Stefan SchneegassHuman-Computer Interaction Group, Paluno-The Ruhr Institute for Software Technology, Faculty of Business Administration and Economics, University of Duisburg-Essen, 45127 Essen, Germany.ORCID 0000-0002-0132-4934
Jens GerkenHuman-Computer Interaction Group, Department of Media Informatics and Communication, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany.ORCID 0000-0002-0634-3931
University of Applied Sciences for Public Administration and Management · DEUniversity of Duisburg-Essen · DE

Funding

Federal Ministry of Education and Research 16SV7866K
6 · The paper itself

Abstract

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they "see" the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot's surrounding have been identified by their sensors. We compared the well-established visualizations

Indexed as

Augmented RealityCaregiversHumansPerceptioncobothuman–robot collaborationprojectionvirtual realityvisualization techniques

Identifiers

PMID35161503
PMCPMC8838221
OpenAlexW4205454783

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.