A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum Disorders †

Lingling Deng, Prapa Rattadilok

Research output: Journal PublicationArticlepeer-review

Abstract

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children’s states and risky environmental factors. Sensory management strategies were recommended to help improve children’s attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children’s performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children’s environment.

Original languageEnglish
Article number5803
JournalSensors
Volume22
Issue number15
DOIs
Publication statusPublished - Aug 2022

Keywords

  • assistive technology
  • autism spectrum disorders
  • fuzzy logic
  • machine learning
  • sensors
  • sensory management
  • wearables

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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