Evidence map›Paper›PMID 40218472›Full record

ReviewSensors (Basel, Switzerland)2025

Low Back Exoskeletons in Industry 5.0: From Machines to Perceiving Co-Pilots-A State-of-the-Art Review.

Andrea Dal Prete, Marta Gandolla, Giuseppe Andreoni, Francesco Braghin

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 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

4 authors.

Andrea Dal PreteMechanical Engineering Department, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.ORCID 0009-0004-2876-1933
Marta GandollaMechanical Engineering Department, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.ORCID 0000-0001-5237-714X
Giuseppe AndreoniMechanical Engineering Department, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.ORCID 0000-0002-5537-4128
Francesco BraghinMechanical Engineering Department, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.ORCID 0000-0002-0476-4118

Funding

This work was partially supported by the Horizon Europe rEUman project under Grant Agreement No.101138930. Grant Agreement No.101138930.
6 · The paper itself

Abstract

This manuscript presents an updated review of back exoskeletons for occupational use, with a particular focus on sensor technology as a key enabler for intelligent and adaptive support. The study aims to identify key barriers to adoption and explore design characteristics which align these systems with the Industry 5.0 paradigm, where machines function as collaborative co-pilots alongside humans. We propose a structured design pipeline and analyze 32 exoskeletons across multiple dimensions, including design, actuation, control strategies, sensor networks, and intelligence. Additionally, we review eight simulation environments which support the early stages of exoskeleton development. Special emphasis is placed on sensor technology, highlighting its critical role in enhancing adaptability and intelligence. Our findings reveal that while 39.39% of exoskeletons accommodate asymmetric activities, kinematic compatibility remains a challenge. Furthermore, only 33.33% of the systems incorporated intelligent features, with just one being capable of adapting its response based on poor posture or real-time human-machine interaction feedback. The limited integration of advanced sensors and decision-making capabilities constrains their potential for dynamic and adaptive support. Open questions remain in high-level decision making, enhanced environmental awareness, and the development of generalizable methods for integrating sensor data into adaptive control strategies.

Indexed as

Exoskeleton DeviceArtificial IntelligenceBiomechanical PhenomenaEquipment DesignHumansRoboticsback exoskeletonintelligent frameworklower-back painwearable technology

Identifiers

PMID40218472
PMCPMC11991514

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.