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Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F21%3A43920903" target="_blank" >RIV/00216208:11120/21:43920903 - isvavai.cz</a>

  • Alternative codes found

    RIV/00064173:_____/20:N0000021 RIV/60461373:22340/21:43923556 RIV/00064173:_____/21:N0000178

  • Result on the web

    <a href="https://doi.org/10.3390/s21010103" target="_blank" >https://doi.org/10.3390/s21010103</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s21010103" target="_blank" >10.3390/s21010103</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis

  • Original language description

    An advanced statistical analysis of patients&apos; faces after specific surgical procedures that temporarily negatively affect the patient&apos;s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House-Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician&apos;s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House-Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30206 - Otorhinolaryngology

Result continuities

  • Project

  • 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

    Sensors

  • ISSN

    1424-8220

  • e-ISSN

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    21

  • Pages from-to

    103

  • UT code for WoS article

    000606098600001

  • EID of the result in the Scopus database

    2-s2.0-85098778618