Evidence map›Paper›PMID 42079821›Full record

ArticleFrontiers in neurology

Digital monitoring of motor function in Parkinson's disease using Markerless motion analysis and exergaming.

Claudia Ferraris, Gianluca Amprimo, Guido Coppo, Francesca Bellanova, Matteo Bigoni, Alessandro Mauro, Lorenzo Priano

Abstract read
In one paragraph

Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Claudia Ferraris *Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Turin, Italy.
Gianluca Amprimo *Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy.
Guido CoppoSynarea Consultants S.R.L., Turin, Italy.
Francesca BellanovaSynarea Consultants S.R.L., Turin, Italy.
Matteo BigoniDivision of Neurology and Neurorehabilitation, S. Giuseppe Hospital, IRCCS Istituto Auxologico Italiano, Piancavallo (VB), Italy.
Alessandro MauroDivision of Neurology and Neurorehabilitation, S. Giuseppe Hospital, IRCCS Istituto Auxologico Italiano, Piancavallo (VB), Italy.
Lorenzo PrianoDivision of Neurology and Neurorehabilitation, S. Giuseppe Hospital, IRCCS Istituto Auxologico Italiano, Piancavallo (VB), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Motor impairment in Parkinson's disease (PD) significantly compromises functional independence. While continuous rehabilitation is crucial, traditional models face logistical and economic barriers that limit continuity of treatments. Methods: To address this challenge, we developed a novel exergaming platform leveraging Google MediaPipe for markerless, real-time kinematic tracking via a standard webcam, eliminating the need for specialized hardware and delivering engaging, gamified physical exercises designed for domestic settings. This study investigates the feasibility, usability, and preliminary clinical impact of a 10-session gaming protocol in 14 out-of-hospital patients. Results: The system showed high technical performance and participant engagement, with an overall trial completion rate exceeding 94% and successful progression through game levels. We observed improvements in key functional parameters, establishing a strong correlation between level progression, measured by the novel Normalized Efficiency Index, and the clinical MDS-UPDRS assessments, both for total ( Discussion: These findings underscore the clinical validity and high acceptance of the proposed solution as a training and remote monitoring tool. By providing granular, longitudinal data, this highly accessible solution offers a promising approach to personalized home-based functional training for people with PD.

Indexed as

AI-assisted rehabilitationdigital medicineexergamingGoogle MediaPipemarkerless body trackingmotion analysisParkinson’s disease

Identifiers

PMID42079821
PMCPMC13130081

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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.