Facial Expression Analysis with Kinect for the Diagnosis of Paralysis Using Nottingham Grading System

Carro, Raúl Cid; Huerta, Edmundo Bonilla; Cruz, Federico Ramírez; Morales-Caporal, Roberto; Corona, Carlos Pérez

doi:10.1109/tla.2016.7587650

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…The SDK 2.0 for the Kinect V2 includes a library for automatically acquiring 3D facial landmarks and Facial Animation Units (FAUs) which reflect the AUs. 3D facial landmarks from the Kinect sensor have been used previously in facial functions' assessment [24][25][26][27][28], and in FP evaluation [29]. FAUs from the Kinect sensor were previously used as a features for facial emotion and expression recognition [30][31][32].…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of facial paralysis based on machine learning techniques

et al. 2022

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Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.

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Section: Feature Extractionmentioning

confidence: 99%

Classification of facial paralysis based on machine learning techniques

et al. 2022

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“…Grading systems for evaluating FNP FDI 20 FaCE 21 Psychosocial scale of facial appearance 22 FACE-Q 23 House-Brackmann 3 Sunnybrook 4 Yanagihara facial nerve grading system 24 FNGS 2.0 25 eFACE 26 Glasgow facial palsy scale 27 Facogram 28 The Nottingham system 29 Moiré topography index 30 Computerized facial analyses 7,[31][32][33][34][35][36][37][38][39] Manual measurement of facial points [40][41][42] Abbreviations: FaCE, facial clinimetric evaluation scale; FDI, facial disability index; FNGS, facial nerve grading system; FNP, facial nerve palsy.…”

Section: Patient Experiencementioning

confidence: 99%

Facial nerve palsy: Narrative review on the importance of the eye and its assessment

et al. 2022

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New solutions are emerging that address specific facial regions in facial nerve palsy (FNP). However the most dreaded consequence of FNP is paralytic lagophthalmos threatening the eye. A way to prioritize these regions is thus required. A review of the literature is conduced to capture the current concepts in evaluating FNP. Overall, patients are assessed from three perspectives: from the clinician's perspective using validated clinician‐based grading instruments, from patient's perspective based on FNP‐specific patient‐reported outcome measures, and from the perspective of the layperson. The existing tools however provide limited information regarding the relative importance of different regions of the face. The eye appears to be an area of great concern for the patient where most surgical therapies are directed at. Addressing ocular problems in FNP carry a high priority but this is not clearly reflected by the standard systems.

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“…ej., Kinect o Leap Motion) ha permitido que se puedan usar como dispositivos de entrada para interfaces de usuario naturales. Dichos sensores se han utilizado con distintos objetivos y para una gran variedad de aplicaciones, como por ejemplo aprendizaje [7]; rehabilitación [8] reconocimiento del lenguaje de signos [9] [10]; o diagnóstico de parálisis facial [11]. La Tabla I muestra las características de los sensores de profundidad comerciales más utilizados, según Cheng et al [9].…”

Section: A Interfaces De Usuario Naturalesunclassified

Developing and Evaluating a Game for the Assessment of Spatial Memory Using Auditory Stimuli

Valencia

Juan

López

et al. 2019

IEEE Latin Am. Trans.

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The combination of natural user interfaces and smart devices has become a new technological option to be exploited for the assessment of spatial memory. In this paper, we present an interactive game for assessing spatial memory using auditory stimuli. The distinct feature of our game is that it assesses the two types of information (i.e., acoustic and spatial) at the same time. We compared the performance of 100 children and 48 adults on the game with traditional neuropsychological tests. The performance on the game correlated with the performance on other traditional neuropsychological tests for the assessment of spatial short-term memory. Children and adults considered our game as easy to use, and all of them were satisfied with it. The results suggest that our game could be a valid tool for the identification of alterations in the spatial memory in both children and adults.

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Facial Expression Analysis with Kinect for the Diagnosis of Paralysis Using Nottingham Grading System

Cited by 12 publications

References 9 publications

Classification of facial paralysis based on machine learning techniques

Classification of facial paralysis based on machine learning techniques

Facial nerve palsy: Narrative review on the importance of the eye and its assessment

Developing and Evaluating a Game for the Assessment of Spatial Memory Using Auditory Stimuli

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