Sensor-based Severity Estimation of Autism Spectrum Disorder to Aid Robot-enhanced Therapy

抄録

In our research, we are attempting to predict Autism Spectrum Disorder (ASD) and the associated Autism Diagnostic Observation Schedule (ADOS) scores using data from the body skeleton, head movement, and eye gaze. To the best of our knowledge, no such prior work has been completed. ASD is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. Scores from the Autism Diagnostic Observation Schedule (ADOS) are regarded as a standard tool for making an early diagnosis of autism. Successful treatment of ASD requires proper diagnosis and methodical therapy plans. Conventional treatments of ASD usually involve diverse intervention techniques designed by professional therapists. Unfortunately, highly trained therapists are not always readily available. Accessible therapists may sometimes lack experience and observational skills, making it difficult to assist ASD children effectively. So the question is, can we find an alternative to Standard Human Therapy (SHT) in the form of Robot Assisted or Robot Enhanced Therapy (RET)? Our work contributes by proposing a RET system based on 3D body joints and gaze information. We investigated the publicly available "DREAM" dataset, having bio-marker information on 61 children diagnosed with ASD. We propose a feature vector that is based on traditional directly connected body joints as well as some unconventional non-attached body joints with close association. We attempted to predict the severity of the disorder based on our predicted ASD levels and ados scores. The goal of our developed system is to effectively assist RET in ASD diagnosis and therapy.