Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10434985" target="_blank" >RIV/00216208:11320/21:10434985 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22340/21:43923548 RIV/00064173:_____/21:N0000169
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=owihRbyy7N" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=owihRbyy7N</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app11104572" target="_blank" >10.3390/app11104572</a>
Alternative languages
Result language
angličtina
Original language name
Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
Original language description
This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House-Brackmann (HB) scale is based on the clinician's subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician's opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6-it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences [online]
ISSN
2076-3417
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
10
Country of publishing house
CH - SWITZERLAND
Number of pages
18
Pages from-to
4572
UT code for WoS article
000662587300001
EID of the result in the Scopus database
2-s2.0-85106946568