Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
| Autor: | Ludmila Verešpejová, Karel Štícha, Pavel Kříž, Kateřina Trnková, Jan Mareš, Jan Kohout, Lenka Červená, Martin Chovanec, Jan Crha, Martin Vejvar |
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| Rok vydání: | 2021 |
| Předmět: |
Technology
QH301-705.5 Computer science QC1-999 rehabilitation 03 medical and health sciences 0302 clinical medicine General Materials Science Biology (General) 030223 otorhinolaryngology QD1-999 Instrumentation functional data analysis Parametric statistics Fluid Flow and Transfer Processes Artificial neural network business.industry Physics Process Chemistry and Technology Supervised learning General Engineering ordinal classification Functional data analysis Statistical model Pattern recognition Engineering (General). Civil engineering (General) House–Brackmann scale Computer Science Applications Random forest Data set Kinect evaluation Chemistry Parametric model Artificial intelligence TA1-2040 business 030217 neurology & neurosurgery |
| Zdroj: | Applied Sciences, Vol 11, Iss 4572, p 4572 (2021) Applied Sciences Volume 11 Issue 10 |
| ISSN: | 2076-3417 |
| Popis: | 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 a classificationan 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 (four HB grades are considered). 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. |
| Databáze: | OpenAIRE |
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