Evidence map›Paper›PMID 35948915›Full record

SynthesisJournal of neuroengineering and rehabilitation2022

Upper limb soft robotic wearable devices: a systematic review.

Elena Bardi, Marta Gandolla, Francesco Braghin, Ferruccio Resta, Alessandra L G Pedrocchi, Emilia Ambrosini

Open access · goldAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
8.8field-weighted citation impact, top 1% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 121 citations in OpenAlex.

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  17. Evaluation of a passive wearable arm ExoNET.Frontiers in robotics and AI · 2024
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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

6 authors at 1 institution in 1 country.

Elena BardiDepartment of Mechanical Engineering, Politecnico di Milano, Milano, Italy. elena.bardi@polimi.it.
Marta GandollaDepartment of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
Francesco BraghinDepartment of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
Ferruccio RestaDepartment of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
Alessandra L G PedrocchiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Emilia AmbrosiniDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Politecnico di Milano · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSoft robotic wearable devices, referred to as exosuits, can be a valid alternative to rigid exoskeletons when it comes to daily upper limb support. Indeed, their inherent flexibility improves comfort, usability, and portability while not constraining the user's natural degrees of freedom. This review is meant to guide the reader in understanding the current approaches across all design and production steps that might be exploited when developing an upper limb robotic exosuit.

methodsThe literature research regarding such devices was conducted in PubMed, Scopus, and Web of Science. The investigated features are the intended scenario, type of actuation, supported degrees of freedom, low-level control, high-level control with a focus on intention detection, technology readiness level, and type of experiments conducted to evaluate the device.

resultsA total of 105 articles were collected, describing 69 different devices. Devices were grouped according to their actuation type. More than 80% of devices are meant either for rehabilitation, assistance, or both. The most exploited actuation types are pneumatic (52%) and DC motors with cable transmission (29%). Most devices actuate 1 (56%) or 2 (28%) degrees of freedom, and the most targeted joints are the elbow and the shoulder. Intention detection strategies are implemented in 33% of the suits and include the use of switches and buttons, IMUs, stretch and bending sensors, EMG and EEG measurements. Most devices (75%) score a technology readiness level of 4 or 5.

conclusionAlthough few devices can be considered ready to reach the market, exosuits show very high potential for the assistance of daily activities. Clinical trials exploiting shared evaluation metrics are needed to assess the effectiveness of upper limb exosuits on target users.

Indexed as

Exoskeleton DeviceRoboticsWearable Electronic DevicesElbowHumansUpper ExtremityAssistive technologyExoskeletonsExosuitsRehabilitation roboticsSoft roboticsUpper limb

Identifiers

PMID35948915
PMCPMC9367113
OpenAlexW4291020950

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.