Evidence map›Paper›PMID 34207306›Full record

ArticleSensors (Basel, Switzerland)2021

Parkinson's Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements.

Filippo Milano, Gianni Cerro, Francesco Santoni, Alessio De Angelis, Gianfranco Miele, Angelo Rodio, Antonio Moschitta, Luigi Ferrigno, Paolo Carbone

Abstract read
In one paragraph

Article 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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. 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

9 authors.

Filippo MilanoDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.ORCID 0000-0003-3811-5084
Gianni CerroDepartment of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.ORCID 0000-0002-6843-7140
Francesco SantoniDepartment of Engineering, University of Perugia, 06125 Perugia, Italy.
Alessio De AngelisDepartment of Engineering, University of Perugia, 06125 Perugia, Italy.ORCID 0000-0001-7953-4272
Gianfranco MieleDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.ORCID 0000-0001-7571-6327
Angelo RodioDepartment of Human, Social and Health Sciences, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
Antonio MoschittaDepartment of Engineering, University of Perugia, 06125 Perugia, Italy.
Luigi FerrignoDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
Paolo CarboneDepartment of Engineering, University of Perugia, 06125 Perugia, Italy.ORCID 0000-0003-0540-1287

Funding

Ministero dell'Istruzione, dell'Università e della Ricerca PRIN 2015C37B25
6 · The paper itself

Abstract

Reliable diagnosis of early-stage Parkinson's disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson's Disease Rating Scale and are provided as case studies to prove the system's capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.

Indexed as

Parkinson DiseaseTremorHumansMagnetic PhenomenaMonitoring, PhysiologicMovementmagnetic positioningmeasurement systemParkinson’s diseasetremor classification

Identifiers

PMID34207306
PMCPMC8235095

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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.