Evidence map›Paper›PMID 40297059›Full record

ArticleWearable technologies2025

Design and showcase of a stairs-based testbed for the benchmark of exoskeleton devices: The STEPbySTEP project.

Marco Caimmi, Nicole Maugliani, Matteo Malosio, Francesco Airoldi, Tito Dinon, Diego Borro, Martxel Eizaguirre, Iñaki Díaz, Sergio Ausejo, Gabriele Puzzo and 5 more

Abstract read
In one paragraph

Article in Wearable technologies, 2025. 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

15 authors.

Marco CaimmiInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of Italian National Research Council (CNR), Milan, Italy.
Nicole MauglianiInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of Italian National Research Council (CNR), Milan, Italy.
Matteo MalosioInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of Italian National Research Council (CNR), Milan, Italy.
Francesco AiroldiInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of Italian National Research Council (CNR), Milan, Italy.
Tito DinonInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of Italian National Research Council (CNR), Milan, Italy.
Diego BorroCEIT-Basque Research and Technology Alliance (BRTA) and Tecnun (University of Navarra), Donostia-San Sebastian, Spain.
Martxel EizaguirreCEIT-Basque Research and Technology Alliance (BRTA) and Tecnun (University of Navarra), Donostia-San Sebastian, Spain.
Iñaki DíazCEIT-Basque Research and Technology Alliance (BRTA) and Tecnun (University of Navarra), Donostia-San Sebastian, Spain.
Sergio AusejoCEIT-Basque Research and Technology Alliance (BRTA) and Tecnun (University of Navarra), Donostia-San Sebastian, Spain.
Gabriele PuzzoDepartment of Psychology, University of Bologna, Bologna, Italy.
Federico FraboniDepartment of Psychology, University of Bologna, Bologna, Italy.
Luca PietrantoniDepartment of Psychology, University of Bologna, Bologna, Italy.
Marco MaccariniDepartment of Innovative Technologies, University of Applied Science and Arts of Southern Switzerland (SUPSI), Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Lugano, Switzerland.
Asad Ali ShahidDepartment of Innovative Technologies, University of Applied Science and Arts of Southern Switzerland (SUPSI), Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Lugano, Switzerland.
Loris RovedaDepartment of Innovative Technologies, University of Applied Science and Arts of Southern Switzerland (SUPSI), Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Lugano, Switzerland.ORCID https://orcid.org/0000-0002-4427-536X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable exoskeletons hold the potential to provide valuable physical assistance across a range of tasks, with applications steadily expanding across different scenarios. However, the lack of universally accepted testbeds and standardized protocols limits the systematic benchmarking of these devices. In response, the STEPbySTEP project, funded within the Eurobench framework, proposes a modular, sensorized, reconfigurable staircase testbed designed as a novel evaluation approach within the first European benchmarking infrastructure for robotics. This testbed, to be incorporated into the Eurobench testing facility, focuses on stairs as common yet challenging obstacles in daily life that provide a unique benchmark for exoskeleton assessment. The primary aim of STEPbySTEP is to propose a modular framework - including a specialized staircase design, tentative metrics, and testing protocols - to aid in evaluating and comparing exoskeleton performance. Here, we present the testbed and protocols developed and validated in preliminary trials using three exoskeletons: two lower-limb exoskeletons (LLEs) and one back-support exoskeleton. The results offer initial insights into the adaptability of the staircase testbed across devices, showcasing example metrics and protocols that underscore its benchmarking potential.

Indexed as

benchmarkevaluation metricsevaluation protocolsexoskeleton

Identifiers

PMID40297059
PMCPMC12034579

What OpenQuestion holds

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LicenceCC BY-NC
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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.