↓ Skip to main content

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Overview of attention for article published in Frontiers in Neuroscience, October 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
2 news outlets
twitter
10 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
285 Dimensions

Readers on

mendeley
543 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review
Published in
Frontiers in Neuroscience, October 2017
DOI 10.3389/fnins.2017.00555
Pubmed ID
Authors

Erika Rovini, Carlo Maremmani, Filippo Cavallo

Abstract

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 543 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 543 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 85 16%
Researcher 76 14%
Student > Master 62 11%
Student > Bachelor 54 10%
Other 33 6%
Other 70 13%
Unknown 163 30%
Readers by discipline Count As %
Engineering 137 25%
Medicine and Dentistry 52 10%
Neuroscience 42 8%
Computer Science 40 7%
Nursing and Health Professions 15 3%
Other 64 12%
Unknown 193 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2020.
All research outputs
#1,560,961
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#759
of 11,542 outputs
Outputs of similar age
#31,003
of 332,159 outputs
Outputs of similar age from Frontiers in Neuroscience
#14
of 174 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,159 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.