Evidence map›Paper›PMID 40732391›Full record

ReviewSensors (Basel, Switzerland)2025

A Comprehensive Review of Elbow Exoskeletons: Classification by Structure, Actuation, and Sensing Technologies.

Callista Shekar Ayu Supriyono, Mihai Dragusanu, Monica Malvezzi

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. 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

3 authors.

Callista Shekar Ayu SupriyonoDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Mihai DragusanuDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-9373-6866
Monica MalvezziDepartment of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.ORCID 0000-0002-2158-5920

Funding

European Union by the Next Generation EU project ECS17 THE - Tuscany Health Ecosystem B63C22000680007
6 · The paper itself

Abstract

The development of wearable robotic exoskeletons has seen rapid progress in recent years, driven by the growing need for technologies that support motor rehabilitation, assist individuals with physical impairments, and enhance human capabilities in both clinical and everyday contexts. Within this field, elbow exoskeletons have emerged as a key focus due to the joint's essential role in upper limb functionality and its frequent impairment following neurological injuries such as stroke. With increasing research activity, there is a strong interest in evaluating these systems not only from a technical perspective but also in terms of user comfort, adaptability, and clinical relevance. This review investigates recent advancements in elbow exoskeleton technology, evaluating their effectiveness and identifying key design challenges and limitations. Devices are categorized based on three main criteria: mechanical structure (rigid, soft, or hybrid), actuation method, and sensing technologies. Additionally, the review classifies systems by their supported range of motion, flexion-extension, supination-pronation, or both. Through a systematic analysis of these features, the paper highlights current design trends, common trade-offs, and research gaps, aiming to guide the development of more practical, effective, and accessible elbow exoskeletons.

Indexed as

ElbowElbow JointExoskeleton DeviceEquipment DesignHumansRange of Motion, ArticularRoboticsWearable Electronic Devicesactuatorassistiveaugmentationelbow exoskeletonrehabilitationsensor

Identifiers

PMID40732391
PMCPMC12299220

What OpenQuestion holds

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LicenceCC BY
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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.