Evidence map›Paper›PMID 32785096›Full record

ArticleSensors (Basel, Switzerland)2020

An Online Method to Detect and Locate an External Load on the Human Body with Applications in Ergonomics Assessment.

Marta Lorenzini, Wansoo Kim, Elena De Momi, Arash Ajoudani

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. 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

4 authors.

Marta Lorenzini1HRI2 Laboratory, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.ORCID 0000-0002-9458-6844
Wansoo Kim1HRI2 Laboratory, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.ORCID 0000-0002-3254-3929
Elena De MomiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-8819-2734
Arash Ajoudani1HRI2 Laboratory, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.ORCID 0000-0002-1261-737X

Funding

European Research Council 850932
6 · The paper itself

Abstract

In this work, we propose an online method to detect and approximately locate an external load induced on the body of a person interacting with the environment. The method is based on a torque equilibrium condition on the human sagittal plane, which takes into account a reduced-complexity model of the whole-body centre of pressure (CoP) along with the measured one, and the vertical component of the ground reaction forces (vGRFs). The latter is combined with a statistical analysis approach to improve the localisation accuracy, (which is subject to uncertainties) to the extent of the industrial applications we target. The proposed technique eliminates the assumption of known contact position of an external load on the human limbs, allowing a more flexible online body-state tracking. The accuracy of the proposed method is first evaluated via a simulation study in which various contact points on different body postures are considered. Next, experiments on human subjects with three different contact locations applied to the human body are presented, revealing the validity of the proposed methodology. Lastly, its benefit in the estimation of human dynamic states is demonstrated. These results add another layer to the online human ergonomics assessment framework developed in our laboratory, extending it to more realistic and varying interaction conditions.

Indexed as

Human BodyPostureComputer SimulationErgonomicsHumansTorquehealth monitoring in working environmentsmonitoring human-environment interactionnovel methods and systems for integrated ergonomic assessment

Identifiers

PMID32785096
PMCPMC7474424

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Registered trials

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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.