Classification of the Tremor signal from Accelerometers and Gyroscopes in Multiple Sclerosis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00368717" target="_blank" >RIV/68407700:21230/23:00368717 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Classification of the Tremor signal from Accelerometers and Gyroscopes in Multiple Sclerosis
Original language description
Tremor, an involuntary rhythmic oscillatory movement of a body part is a common problem for patients suffering from multiple sclerosis (MS). This paper aims to use accelerometric and gyroscopic measurements of postural tremor from the upper limbs to determine whether a patient suffers from MS. The used data includes signals from a group of 16 MS patients (3 males and 13 females) and a group of 18 healthy control subjects (9 males and 9 females). Methods involving neural networks were used for signal classification from the power spectral density (PSD) of the given signals. Different fully-connected neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN) architectures were explored. The best reached results were a recall of 100% and a precision of 89%, achieved by one of the proposed CNN models.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2023 International Conference on Applied Electronics
ISBN
979-8-3503-3553-8
ISSN
1803-7232
e-ISSN
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Number of pages
4
Pages from-to
67-70
Publisher name
Západočeská univerzita v Plzni
Place of publication
Plzeň
Event location
Plzeň
Event date
Sep 6, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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