Evidence map›Paper›PMID 30841656›Full record

ArticleSensors (Basel, Switzerland)2019

Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson's Disease.

Claudia Ferraris, Roberto Nerino, Antonio Chimienti, Giuseppe Pettiti, Nicola Cau, Veronica Cimolin, Corrado Azzaro, Lorenzo Priano, Alessandro Mauro

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

  1. Pooled it
  2. Article
  3. Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025
    Review
  4. Review
  5. Detecting the symptoms of Parkinson's disease with non-standard video.Journal of neuroengineering and rehabilitation · 2024
    Article
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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

9 authors.

Claudia FerrarisInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. claudia.ferraris@ieiit.cnr.it.
Roberto NerinoInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. roberto.nerino@ieiit.cnr.it.
Antonio ChimientiInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. antonio.chimienti@ieiit.cnr.it.
Giuseppe PettitiInstitute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. giuseppe.pettiti@ieiit.cnr.it.
Nicola CauIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy. n.cau@auxologico.it.
Veronica CimolinDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. veronica.cimolin@polimi.it.
Corrado AzzaroIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy. c.azzaro@auxologico.it.
Lorenzo PrianoIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy. lorenzo.priano@unito.it.
Alessandro MauroIstituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy. alessandro.mauro@unito.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A self-managed, home-based system for the automated assessment of a selected set of Parkinson's disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson's Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson's disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects' performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson's disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

Indexed as

AgedBiomechanical PhenomenaFemaleHumansLower ExtremityMachine LearningMaleMiddle AgedParkinson DiseasePostural BalanceUser-Computer Interfaceat-home monitoringautomated assessmentcenter of massmachine learningmovement disordersneurorehabilitationParkinson’s diseasepostural stabilitypostureRGB-depthUPDRS tasks

Identifiers

PMID30841656
PMCPMC6427119

What OpenQuestion holds

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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.