Integrating Behavior of Children with Profound Intellectual, Multiple, or Severe Motor Disabilities With Location and Environment Data Sensors for Independent Communication and Mobility: App Development and Pilot Testing.
Von Ralph Dane Marquez HerbuelaTomonori KaritaYoshiya FurukawaYoshinori WadaYoshihiro YagiShuichiro SenbaEiko OnishiTatsuo SaekiPublished in: JMIR rehabilitation and assistive technologies (2021)
The ChildSIDE app is an effective tool in collecting the behavior data of children with PIMD/SMID. The app showed high server/API performance in detecting outdoor location and environment data from sensors and an online API to the database with a performance rate above 93%. The results of the analysis and categorization of behaviors suggest a need for a system that uses motion capture and trajectory analyses for developing machine- or deep-learning algorithms to predict the needs of children with PIMD/SMID in the future.
Keyphrases
- deep learning
- young adults
- electronic health record
- machine learning
- big data
- clinical trial
- emergency department
- randomized controlled trial
- study protocol
- small molecule
- data analysis
- intellectual disability
- convolutional neural network
- particulate matter
- early onset
- autism spectrum disorder
- mass spectrometry
- high speed
- protein protein
- double blind