Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1929
Title: “SenseA”-Autism Early Signs and Pre-Aggressive Detector Through Image Processing
Authors: Gamaethige, C
Gunathilake, U
Jayasena, D
Manike, H
Samarasinghe, P
Yatanwala, T
Keywords: SenseA
Autism Early Signs
Pre-Aggressive
Detector through
Image Processing
Issue Date: 4-Dec-2017
Publisher: IEEE
Citation: C. Gamaethige, U. Gunathilake, D. Jayasena, H. Manike, P. Samarasinghe and T. Yatanwala, "“SenseA”-Autism Early Signs and Pre-Aggressive Detector through Image Processing," 2017 Asia Modelling Symposium (AMS), 2017, pp. 125-130, doi: 10.1109/AMS.2017.28.
Series/Report no.: 2017 Asia Modelling Symposium (AMS);Pages 125-130
Abstract: This paper presents an efficient solution for the current problem of identifying early signs of autism and detecting pre aggressive behaviours using videos which can be used to produce a more convenient environment for autistic children and their caregivers. Early detection of autism spectrum disorder and its consequences play major role in intervention. Yet it often remains unrecognized and diagnosed in non-clinical environments because of unawareness and the lack of screening tools specific to the autism. At times, autistic children express their feelings through aggressive behaviour towards themselves or other children due to number of reasons such as failing to understand their own feelings, misunderstanding and severe distress. While an autistic child engages in physical aggression, an immediate response is required because the sibling or peer will likely react to the child's aggressive behaviour. There's no systematic approach to identify the pre-aggressive behaviour of autistic children in software engineering perspective. The proposed Autism Spectrum Disorder (ASD) early signs and pre-aggressive detection system is a software which provides automated solution to aforementioned problems using computer vision and machine learning libraries. It provides a level detection on early signs of autism by analyzing facial features and behaviour patterns in non-clinical perspective. Finally it detects the pre aggressive behaviours of autistic children and alerts the relevant authorized individual using a mobile notification.
URI: http://rda.sliit.lk/handle/123456789/1929
ISSN: 2376-1172
Appears in Collections:Department of Information Technology-Scopes
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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