Evidence map›Paper›PMID 41731263›Full record

ArticleAnnals of biomedical engineering2026

Design and Preliminary Evaluation of a Smart Orthotic Videogame Controller Dedicated to Children.

Fabio Lazzari, Jacopo Romanò, Roberta Nossa, Sara Meloni, Lorenzo Garavaglia, Eleonora Diella, Matteo Valoriani, Francesca Fedeli, Matteo Porro, Emilia Biffi and 1 more

Abstract read
In one paragraph

Article in Annals of biomedical engineering, 2026. 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

11 authors.

Fabio LazzariInstitute of Condensed Matter Physics and Technologies for Energy (CNR-ICMATE), National Research Council of Italy, Lecco, Italy. fabio.lazzari@cnr.it.ORCID http://orcid.org/0000-0003-4072-6431
Jacopo RomanòInstitute of Condensed Matter Physics and Technologies for Energy (CNR-ICMATE), National Research Council of Italy, Lecco, Italy.ORCID http://orcid.org/0000-0003-0347-401X
Roberta NossaScientific Institute, IRCCS Eugenio Medea, Bosisio Parini (LC), Italy.ORCID http://orcid.org/0000-0003-3055-2327
Sara MeloniPediatric Physical Medicine & Rehabilitation Service, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Lorenzo GaravagliaInstitute of Condensed Matter Physics and Technologies for Energy (CNR-ICMATE), National Research Council of Italy, Lecco, Italy.ORCID http://orcid.org/0000-0003-1757-5276
Eleonora DiellaScientific Institute, IRCCS Eugenio Medea, Bosisio Parini (LC), Italy.ORCID http://orcid.org/0000-0003-2756-9355
Matteo ValorianiFifthingenium, Milan, Italy.
Francesca FedeliFightTheStroke Foundation, Milan, Italy.
Matteo PorroPediatric Physical Medicine & Rehabilitation Service, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.ORCID http://orcid.org/0000-0001-9160-9986
Emilia BiffiScientific Institute, IRCCS Eugenio Medea, Bosisio Parini (LC), Italy.ORCID http://orcid.org/0000-0002-2568-9735
Simone PittaccioInstitute of Condensed Matter Physics and Technologies for Energy (CNR-ICMATE), National Research Council of Italy, Lecco, Italy.ORCID http://orcid.org/0000-0001-5070-4602

Funding

Fondazione Cariplo 2018/1471Ministero della Salute Ricerca Corrente 2024Ministero dell'Università e della Ricerca PNC0000007
6 · The paper itself

Abstract

This paper describes the development, fabrication and testing of Playcuff, a wearable device designed to act as a videogame controller for children with motor disabilities, which also provides an orthotic action to improve the control of the upper limb. The aim of this device is to empower children with motor impairment and enable them to access and enjoy gaming despite their disabilities. The videogame controller function was achieved through on-board gesture classification using a two-tiered Fine Tree machine learning algorithm integrated into the device's firmware. Based on features extracted from two inertial sensors present on the device, the classifier was trained to identify in real time 22 classes representing different postures and movements of forearm and wrist, showing an accuracy higher than 94%. A cohort of children (n = 19, aged 9.01 ± 1.95 years old) with neuromotor impairment involving the upper limb were enrolled to test the device. The acceptability and effectiveness of the device were evaluated through a specific questionnaire: the resulting answers were heavily skewed towards appreciation (80.5%) rather than criticism. The methods of classification were found to be simple and effective in controlling the game. In conclusion, Playcuff was shown to be a versatile and well-received orthotic controller, which could be used in future also for videogame-based rehabilitation.

Indexed as

Orthotic DevicesVideo GamesWearable Electronic DevicesChildEquipment DesignFemaleHumansMachine LearningMaleExergame-based rehabilitationGesture classificationIMU sensorsMachine learningWearable orthoticsWireless controller

Identifiers

PMID41731263
PMCPMC13291054

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.