Adaptation and Validation of a Serious Game for Motor Learning Training in Children with Cerebral Palsy

Objective: Children with cerebral palsy (CP) present motor learning disorders and somatosensory dysfunction. Although many protocols use videogames in children with CP, few apply or examine motor learning principles. This study aims at (1) implementing therapist-user-designer collaboration in adapting a videogame to the principles of motor learning and the characteristics of users with CP, and (2) piloting the effectiveness of these adaptations by analyzing the achievement of motor learning parameters (learning rate acquisition, retention, and transfer to motor and somatosensory function). Materials and Methods: Periodical interprofessional meetings conducted to the adaptation of a videogame, requiring the control of a joystick for traveling through a maze, to motor learning principles. In a pilot validation, effects in unilateral upper limb function, gross manual dexterity, and somatosensory thresholds were assessed before and after 10-week training in 13 children with CP. Results: After 10-week training with the adapted serious game, children showed learning rates above 90% and improvement in motor learning parameters along the sessions. Manual dexterity and pronation-supination of the dominant hand improved after training. No significant effects were found on somatosensory thresholds. Conclusion: Serious games are useful as motor learning tools for improving motor function in children with PC. Cooperative work among professionals and users is advisable for designing efficient videogames according to rehabilitation best practices.

Laughter and smiling facial expression modelling for the generation of virtual affective behavior

Laughter and smiling are significant facial expressions used in human to human communication. We present a computational model for the generation of facial expressions associated with laughter and smiling in order to facilitate the synthesis of such facial expressions in virtual characters. In addition, a new method to reproduce these types of laughter is proposed and validated using databases of generic and specific facial smile expressions. In particular, a proprietary database of laugh and smile expressions is also presented. This database lists the different types of classified and generated laughs presented in this work. The generated expressions are validated through a user study with 71 subjects, which concluded that the virtual character expressions built using the presented model are perceptually acceptable in quality and facial expression fidelity. Finally, for generalization purposes, an additional analysis shows that the results are independent of the type of virtual character’s appearance.

Using a Social Robot to Evaluate Facial Expressions in the Wild

In this work an affective computing approach is used to study the human-robot interaction using a social robot to validate facial expressions in the wild. Our global goal is to evaluate that a social robot can be used to interact in a convincing manner with human users to recognize their potential emotions through facial expressions, contextual cues and bio-signals. In particular, this work is focused on analyzing facial expression. A social robot is used to validate a pre-trained convolutional neural network (CNN) which recognizes facial expressions. Facial expression recognition plays an important role in recognizing and understanding human emotion by robots. Robots equipped with expression recognition capabilities can also be a useful tool to get feedback from the users. The designed experiment allows evaluating a trained neural network in facial expressions using a social robot in a real environment. In this paper a comparison between the CNN accuracy and human experts is performed, in addition to analyze the interaction, attention and difficulty to perform a particular expression by 29 non-expert users. In the experiment, the robot leads the users to perform different facial expressions in motivating and entertaining way. At the end of the experiment, the users are quizzed about their experience with the robot. Finally, a set of experts and the CNN classify the expressions. The obtained results allow affirming that the use of social robot is an adequate interaction paradigm for the evaluation on facial expression.