Evidence map›Paper›PMID 38486626›Full record

ArticleWearable technologies2021

Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.

Tommaso Poliero, Matteo Sposito, Stefano Toxiri, Christian Di Natali, Matteo Iurato, Vittorio Sanguineti, Darwin G Caldwell, Jesús Ortiz

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Wearable technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

12 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Tommaso PolieroDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.ORCID https://orcid.org/0000-0002-3901-8983
Matteo SpositoDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.ORCID https://orcid.org/0000-0002-7442-1399
Stefano ToxiriDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
Christian Di NataliDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.ORCID https://orcid.org/0000-0001-7399-7399
Matteo IuratoDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy.
Vittorio SanguinetiDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy.
Darwin G CaldwellDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
Jesús OrtizDepartment of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
Italian Institute of Technology · ITUniversity of Genoa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users' subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to in lifting and in carrying. However, the non-versatile control strategy hindered the users' natural gait (e.g., reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.

Indexed as

carryinghuman activity recognitionliftingmanual material handlingoccupational exoskeletons

Identifiers

PMID38486626
PMCPMC10936340
OpenAlexW3198963453

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.