Evidence map›Paper›PMID 34696076›Full record

ReviewSensors (Basel, Switzerland)2021

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.

Daniele Esposito, Jessica Centracchio, Emilio Andreozzi, Gaetano D Gargiulo, Ganesh R Naik, Paolo Bifulco

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. An Embedded Electromyogram Signal Acquisition Device.Sensors (Basel, Switzerland) · 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.

Daniele EspositoDepartment of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0003-0716-8431
Jessica CentracchioDepartment of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0003-3422-8727
Emilio AndreozziDepartment of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0003-4829-3941
Gaetano D GargiuloSchool of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia.ORCID 0000-0003-1790-9838
Ganesh R NaikSchool of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia.
Paolo BifulcoDepartment of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0002-9585-971X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a definition, Human-Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs' complexity, so their usefulness should be carefully evaluated for the specific application.

Indexed as

RoboticsVirtual RealityHumansSurveys and Questionnairesassistive technologybiosignalscommunicationgesture recognitionHuman–Machine Interfaceprosthetic controlrehabilitationrobotic controlsmart environment controlvirtual reality control

Identifiers

PMID34696076
PMCPMC8540117

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.