Evidence map›Paper›PMID 36365870›Full record

ArticleSensors (Basel, Switzerland)2022

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect.

Gianluca Amprimo, Giulia Masi, Lorenzo Priano, Corrado Azzaro, Federica Galli, Giuseppe Pettiti, Alessandro Mauro, Claudia Ferraris

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

  1. Pooled it
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  3. [Research progress in motor assessment of neurodegenerative diseases driven by motion capture data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
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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

8 authors.

Gianluca AmprimoInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.ORCID 0000-0003-4061-8211
Giulia MasiDepartment of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.
Lorenzo PrianoDepartment of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.
Corrado AzzaroIstituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy.
Federica GalliIstituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy.
Giuseppe PettitiInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.ORCID 0000-0003-0547-0143
Alessandro MauroDepartment of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy.ORCID 0000-0001-9072-7454
Claudia FerrarisInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.ORCID 0000-0001-5381-4794

Funding

POR F.E.S.R. 2014/2020 - Piattaforma Tecnologica Salute e Benessere from Regione Piemonte (Italy) ReHome-Soluzioni ICT per la tele-riabilitazione di disabilità cognitive e motorie originate da patologie neurologiche
6 · The paper itself

Abstract

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.

Indexed as

Parkinson DiseaseVirtual RealityExergamingHumansMovementQuality of Lifeazure kinectbody trackingexergamesmovement analysisneurorehabilitationParkinson’s diseasetelemedicineUPDRS

Identifiers

PMID36365870
PMCPMC9654712

What OpenQuestion holds

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Registered trials

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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.