Authors:
Maria Mejía-Trujillo
;
Faiber Camelo-Romero
;
Helio Ramírez-Arévalo
and
Miguel Feijóo-García
Affiliation:
Program of Systems Engineering, Universidad El Bosque, Bogotá, Colombia
Keyword(s):
Healthcare Information Systems, Recommendation Systems, Gestural Analysis, Machine Learning.
Abstract:
Autism Spectrum Disorder is a neurological condition that affects 1 in 160 children worldwide. To date, this disorder does not yet have a standardized cure, and not being treated early can affect the child’s quality of life and their relatives. There are currently different traditional tools for detecting Autism Spectrum Disor- der, such as questionnaires and checklists— standardized methods worldwide, such as using M-CHAT-R/F and Q-CHAT. We present GesTEApp as a web-based expert system that integrates gestural analytics and sup- ports Healthcare Professionals in their medical decision-making process on the early detection of this disorder in children. GesTEApp implements a Hybrid Recommendation System with Face Recognition models and Linear Kernel, which capture and analyze children’s facial expressions, seeking to support Healthcare Profes- sionals in detecting Autism Spectrum Disorder. We evaluated this tool following a pilot study and reported the findings and results considering
Healthcare Professionals’ perceptions, basing our analysis on (1-5) Lik- ert Scales and their feedback regarding their experience interacting with GesTEApp. Preliminary, the tool reduced detection times by 36% compared to traditional tools. Also, our preliminary results suggest that GesTEApp is a user-centered web-based application that satisfactorily supports Healthcare Professionals in detecting Autism Spectrum Disorder in children.
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