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A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2019
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4 tweeters

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33 Dimensions

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96 Mendeley
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Title
A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease
Published in
BMC Medical Informatics and Decision Making, December 2019
DOI 10.1186/s12911-019-0987-5
Pubmed ID
Authors

Domenico Buongiorno, Ilaria Bortone, Giacomo Donato Cascarano, Gianpaolo Francesco Trotta, Antonio Brunetti, Vitoantonio Bevilacqua

Abstract

Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society - sponsored revision of Unified Parkinson's Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 16%
Researcher 12 13%
Student > Master 11 11%
Student > Bachelor 11 11%
Student > Doctoral Student 5 5%
Other 15 16%
Unknown 27 28%
Readers by discipline Count As %
Computer Science 19 20%
Medicine and Dentistry 12 13%
Engineering 12 13%
Neuroscience 11 11%
Nursing and Health Professions 3 3%
Other 7 7%
Unknown 32 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 18 December 2019.
All research outputs
#10,091,067
of 16,414,025 outputs
Outputs from BMC Medical Informatics and Decision Making
#970
of 1,492 outputs
Outputs of similar age
#214,414
of 386,672 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#102
of 168 outputs
Altmetric has tracked 16,414,025 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,492 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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 386,672 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 168 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.