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Master's Dissertation
DOI
10.11606/D.18.2017.tde-14112017-102247
Document
Author
Full name
Viviane Cristina Roma Appel
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2014
Supervisor
Committee
Caurin, Glauco Augusto de Paula (President)
Delbem, Alexandre Cláudio Botazzo
Krebs, Hermano Igo
Title in Portuguese
Classificando emoções em processos de reabilitação robótica
Keywords in Portuguese
Classificação de emoções
Computação afetiva
Imagens térmicas faciais
Reabilitação robótica
Abstract in Portuguese
Reabilitação robótica tem um papel importante em exercícios terapêuticos ao combinar robôs com jogos sérios de computador em uma atraente plataforma terapêutica. Entretanto, a tarefa de medir o grau de adesão do paciente ao tratamento não é trivial. A dificuldade de aplicar técnicas baseadas em questionários e entrevistas, particularmente em pacientes que tiveram a fala comprometida por acidente vascular encefálico (AVE), nos inspirou a investigar técnicas não verbais e não invasivas para classificar emoções. Com este propósito, uma rede neural supervisionada foi projetada para interpretar imagens térmicas infravermelhas faciais de indivíduos realizando terapia robótica de reabilitação integrada com os jogos. Uma base de dados contendo imagens de 8 voluntários foi criada e contém reações emocionais espontâneas e provocadas. No total, foram analizadas 2445 imagens térmicas faciais, em média 100 imagens por pessoa por 3 categorias de emoções (neutra, motivado e sobrecarregado). Baseado em análise de matriz de confusão, os resultados experimentais se correlacionaram muito bem com as estimativas manuais, produzindo um desempenho global de 92,6%.
Title in English
Classifying emotions in rehabilitation robotics based on facial skin temperature
Keywords in English
Affective computing
Emotion cognition
Facial thermal images
Rehabilitation robotic
Abstract in English
Rehabilitation robotic plays an important role in therapeutic exercises by combining robots with computer serious games into an attractive therapeutic platform. However, measuring the degree of engagement of the user is not a trivial task. The difficulty of applying question-based techniques, particularly for patients who have the speech capacity compromised due to cerebrovascular accidents, has inspired us to investigate noninvasive and nonverbal techniques aiming to classifying emotions. For this purpose, a supervised artificial neural network interprets facial infrared thermal images of individuals performing rehabilitation robotic therapy integrated with games. A database containing images of 8 users was generated by combining evoked and spontaneous emotional reactions. In total, 2445 facial thermal images with an average of 100 images per person for three categories of emotions (neutral, motivated, and overstressed) were analyzed. Based on confusion matrix analysis, the experimental results correlated very well with manual estimates, producing an overall performance of 92.6%.
 
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Publishing Date
2017-11-14
 
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