Evidence map›Paper›PMID 40242512›Full record

ArticleFrontiers in robotics and AI2025

Development of a neural efficiency metric to assess human-exoskeleton adaptations.

Ranjana K Mehta, Yibo Zhu, Eric B Weston, William S Marras

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. 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.

Ranjana K MehtaDepartment of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI, United States.
Yibo ZhuDepartment of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
Eric B WestonDepartment of Integrated Systems Engineering, The Ohio State University, Columbus, OH, United States.
William S MarrasDepartment of Integrated Systems Engineering, The Ohio State University, Columbus, OH, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Passive exoskeletons have been introduced to alleviate loading on the lumbar spine while increasing the wearer's productivity. However, few studies have examined the neurocognitive effects of short-term human-exoskeleton adaptation. The objective of the study was to develop a novel neural efficiency metric to assess short-term human exoskeleton adaptation during repetitive lifting. Twelve participants (gender-balanced) performed simulated asymmetric lifting tasks for a short duration (phase: early, middle, late) with and without a passive low back exoskeleton on two separate days. Phase, exoskeleton condition, and their interaction effects on biomechanical parameters, neural activation, and the novel neural efficiency metric were examined. Peak L5/S1 superior lateral shear forces were found to be significantly lower in the exoskeleton condition than in the control condition. However, other biomechanical and neural activation measures were comparable between conditions. The temporal change of the neural efficiency metric was found to follow the motor adaptation process. Compared to the control condition, participants exhibited lower efficiency during the exoskeleton-assisted lifting condition over time. The neural efficiency metric was capable of tracking the short-term task adaptation process during a highly ambulatory exoskeleton-assisted manual handling task. The exoskeleton-assisted task was less efficient and demanded a longer adaptation period than the control condition, which may impact exoskeleton acceptance and/or intent to use.

Indexed as

biomechanicsexoskeletonfNIRSliftingmotor adaptationneuroergonomics

Identifiers

PMID40242512
PMCPMC11999848

What OpenQuestion holds

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LicenceCC BY
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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.