AutisMitr: Emotion Recognition Assistive Tool for Autistic Children (original) (raw)
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Face Emotion Based Training System for Childrens with Autism
2022
As of today, children diagnosed with Autism Spectrum Disorder (ASD) are becoming an increasingly common occurrence in our schools and society. Consequently, this increases the need to develop assistive devices for ASD children. This paper shows the development of a system designed to facilitate learning in ASD children. This Arduino-based game is equipped with common components such as touch sensor, MP3 player and LEDs to increase replicability. A research was done based on the Early Intervention module to develop a game that could help improve cognitive skill of ASD children. Early Interventions for children with ASD has proven to be effective in reducing ASD symptoms. With the advancement of technology, a wide range of automated tools are now used to teach children with autism. One of the widely used therapies for children with Autism Spectrum Disorder is Applied Behaviour Analysis (ABA) training that focuses on improving a wide range of behaviours like communication, adaptive learning skills, social skills and a variety of motor skills. Thus, the objective of this article is to design and develop a gaming application for autistic children for improving their cognitive skills The cognitive development (in terms of gaming scores) of a child over the time can be stored and analyzed using this application. A light-weighted evaluation study was carried out; and found that the proposed gaming application is usable, effective and useful for autistic kids to improve their cognitive skills.
Emotion recognition system for autism disordered people
Journal of Ambient Intelligence and Humanized Computing, 2019
People with autism spectrum disorders have difficulties with communicating and socially interacting through facial expressions, even with their parents. The proposed approach applies person identification and emotion recognition. The objective of this work is to monitor and identify the people with autism spectral disorder based on sensors and machine learning algorithm. Our proposed system uses neurological sensor to collect the EEG data of patients and Q sensor for measuring stress level. The proposal integrates the facial recognition for identifying emotion recognition. The experimental results obtained from the proposed work performance evaluation are discussed, considering each for Autism Patient and the emotion labels. Proposed work shown the experimental results that can detect emotion with good accuracy compared to other classifiers. The proposed work achieves a 6% better accuracy for Proposed Model than Support Vector machine and 8% more accuracy than back Propagation algorithm.
Recognition of Emotions for People with Autism: An Approach to Improve Skills
International Journal of Computer Games Technology
Autism spectrum disorder refers to a neurodevelopmental disorders characterized by repetitive behavior patterns, impaired social interaction, and impaired verbal and nonverbal communication. The ability to recognize mental states from facial expressions plays an important role in both social interaction and interpersonal communication. Thus, in recent years, several proposals have been presented, aiming to contribute to the improvement of emotional skills in order to improve social interaction. In this paper, a game is presented to support the development of emotional skills in people with autism spectrum disorder. The software used helps to develop the ability to recognize and express six basic emotions: joy, sadness, anger, disgust, surprise, and fear. Based on the theory of facial action coding systems and digital image processing techniques, it is possible to detect facial expressions and classify them into one of the six basic emotions. Experiments were performed using four pub...
An android for enhancing social skills and emotion recognition in people with autism
Neural Systems and …, 2005
It is well documented that the processing of social and emotional information is impaired in people with autism. Recent studies have shown that individuals, particularly those with high functioning autism, can learn to cope with common social situations if they are made to enact possible scenarios they may encounter in real life during therapy. The main aim of this work is to describe an interactive life-like facial display (FACE) and a supporting therapeutic protocol that will enable us to verify if the system can help children with autism to learn, identify, interpret, and use emotional information and extend these skills in a socially appropriate, flexible, and adaptive context. The therapeutic setup consists of a specially equipped room in which the subject, under the supervision of a therapist, can interact with FACE. The android display and associated control system has automatic facial tracking, expression recognition, and eye tracking. The treatment scheme is based on a series of therapist-guided sessions in which a patient communicates with FACE through an interactive console. Preliminary data regarding the exposure to FACE of two children are reported.
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility, 2008
Many first-hand accounts from individuals diagnosed with autism spectrum disorders (ASD) highlight the challenges inherent in processing high-speed, complex, and unpredictable social information such as facial expressions in real-time. In this paper, we describe a new technology aimed at helping people capture, analyze, and reflect on a set of social-emotional signals communicated by facial and head movements in live social interaction that occurs with their everyday social companions. We describe our development of a new combination of hardware using a miniature camera connected to an ultramobile PC together with custom software developed to track, capture, interpret, and intuitively present various interpretations of the facial-head movements (e.g., presenting that there is a high probability the person looks "confused"). This paper describes this new technology together with the results of a series of pilot studies conducted with adolescents diagnosed with ASD who used the technology in their peer-group setting and contributed to its development via their feedback. Categories and Subject Descriptors J.4 [Computer Applications]: Social and behavioral sciencespsychology.
Augmented reality emotion recognition for autism spectrum disorder children
F1000Research, 2021
Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that affects brain development. The prevalence of ASD is one in 68 children. Low social motivation is the main cause in developing social communication skills deficiency. As a result, it is becoming difficult for them to express themselves, to be able to manage social interactions, and they lack the ability to comfort others and even share their own feelings. This study aimed to design a mobile application based on augmented reality (AR) focusing on social interactions and communication aspect for children with ASD. The scope is in emotion recognition, which makes use of emotional icons to help them improve their social skills, more specifically on helping them to recognize various emotions. The emotions are represented by emojis inspired by Dr. Paul Ekman who has created the basic six emotions, namely happiness, sadness, disgust, fear, surprise, and angry. Additional emotions such as confound face, winking wit...
International Journal of Internet Education, 2018
This work presents a game titled Face: a game to aid treatment of autistic children through the interpretation and recognition of facial expressions, developed by the team multidisplinar of the Federal Institute of Education, Science and Technology of Maranhão (IFMA), using the methodology of Applied Behavior Analysis (ABA), commonly associated with the treatment of children with Disorders of the Autism Spectrum disorders (ASD). The application divide the learning of facial expression in small tasks, containing positive reinforcement each time the child wins or loses. The educator has access to the settings menu of the game, can increase or reduce the amount of interaction between the child and the game can select the facial expression to be worked. The first screen of the game displays an image chosen by the educator, presented a screen with "n" bins "m", in order to improve the learning of the facial expression desired. After the child be able to reach all the "n" screen positions, more interaction will be added with the goal of strengthening the Mobile computing tool to help treat autistic children with difficulties in recognizing ......
Facial Emotions Based PC Access for The Benefit of Autistic People
Face recognition refers to an individual's understanding and interpretation of the human face especially in relation to the associated information processing in the brain. Autism Spectrum Disorder (ASD) is a comprehensive neural developmental disorder that produces many deficits including social, communicative and perceptual. Individuals with autism exhibit difficulties in various aspects of facial perception, including facial identity recognition and recognition of emotional expressions. Autism Spectrum Disorders (ASD) are characterized by atypical patterns of behaviors and impairments in social communication. Traditional intervention approaches often require intensive support and well-trained therapists to address core deficits. People with ASD have tremendous difficulty accessing such care due to lack of available trained therapists as well as intervention costs. Thus a Human Facial Emotions based image processing system is to be developed which processes autistic people’s expressions and enables them to access PC applications based on their expressions.
A Facial Affect Analysis System for Autism Spectrum Disorder
2019 IEEE International Conference on Image Processing (ICIP), 2019
In this paper, we introduce an end-to-end machine learningbased system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence. Our system classifies ASD using representations of different facial attributes from convolutional neural networks, which are trained on images in the wild. Our experimental results show that different facial attributes used in our system are statistically significant and improve sensitivity, specificity, and F1 score of ASD classification by a large margin. In particular, the addition of different facial attributes improves the performance of ASD classification by about 7% which achieves a F1 score of 76%.