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Balikis Banire, a graduate of Electronics and Computer Engineering at the Lagos State University, is on currently researching on how technology can be used in detecting teaching needs in educating children suffering from autism.
The research is a collaboration of the works of Balikis, a PhD student in computer science and engineering at Qatar Foundation (QF), Dr Dena Al Thani and Dr Marwa Qaraqe, both Assistant Professors of Information and Computing Technology at HBKU. The project also involved Dr Bilal Mansoor, Assistant Professor of Mechanical Engineering at Texas A&M University at Qatar (TAMUQ) as well as teachers and experts at Renad Academy – an innovative Pre-University Education (PUE) centre set-up to non-invasively measure the attention span of students with autism spectrum disorder (ASD).
Autism is a pervasive neurological disorder that is observable in early childhood and persists throughout the lifespan, characterised by atypical communication, language development, eye contact, and sensory experiences. This research is borne out of the need to investigate the required technologies that can be used in knowing the educational needs of a child suffering from autism.
Balikis Banire is the middle with her project supervisors
It employs the use of commercially available facial recognition software and a webcam to track changes in facial expression and eye gaze that occur while children are engaged in an attention test.
Empirically, it combines the use of facial expression and eye tracking to gauge the attention span of children with ASD. It then stylistically adopts a means of measuring attention in a non-noticeable manner as affected children easily have a heightened sensory response in certain settings which may cause a breakdown in learning if attention monitoring is not done with the use of gadgets involving physical contact.
Banire et al hypothesized that changes in expression normally happen on facial landmarks or ‘hotspots’ on the face, such as eyebrows and cheekbones where the raw data of the facial landmark together with eye tracking from the attention test are fed into an algorithm that will be used to design a model to predict and monitor attention.
Significantly, the research through the use of a contactless approach has provided for teachers/instructors an overview in foreseeing either positive or negative reaction changes in autism children while learning is taking place. Moreover, changes in facial expression and eyes tracking can also trigger a warning that alerts a student with autism, who may be distracted, to refocus on the task at hand.