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Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Overview of attention for article published in PLoS ONE, February 2017
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4 tweeters

Citations

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46 Dimensions

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142 Mendeley
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Title
Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer
Published in
PLoS ONE, February 2017
DOI 10.1371/journal.pone.0171764
Pubmed ID
Authors

Daniel Rodríguez-Martín, Albert Samà, Carlos Pérez-López, Andreu Català, Joan M. Moreno Arostegui, Joan Cabestany, Àngels Bayés, Sheila Alcaine, Berta Mestre, Anna Prats, M. Cruz Crespo, Timothy J. Counihan, Patrick Browne, Leo R. Quinlan, Gearóid ÓLaighin, Dean Sweeney, Hadas Lewy, Joseph Azuri, Gabriel Vainstein, Roberta Annicchiarico, Alberto Costa, Alejandro Rodríguez-Molinero

Abstract

Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

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 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 142 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 27%
Researcher 20 14%
Student > Master 15 11%
Student > Bachelor 11 8%
Student > Doctoral Student 8 6%
Other 21 15%
Unknown 28 20%
Readers by discipline Count As %
Engineering 48 34%
Neuroscience 19 13%
Computer Science 11 8%
Medicine and Dentistry 10 7%
Psychology 5 4%
Other 13 9%
Unknown 36 25%

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 21 February 2017.
All research outputs
#6,021,924
of 10,571,772 outputs
Outputs from PLoS ONE
#68,789
of 124,807 outputs
Outputs of similar age
#137,605
of 256,346 outputs
Outputs of similar age from PLoS ONE
#2,360
of 4,276 outputs
Altmetric has tracked 10,571,772 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 124,807 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 40th percentile – i.e., 40% 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 256,346 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,276 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.