Deep Mobile Linguistic Therapy for Patients with ASD

Ari Ernesto Ortiz Castellanos, Chuan-Ming Liu () and Chongyang Shi ()
Additional contact information
Ari Ernesto Ortiz Castellanos: College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei City 106, Taiwan
Chuan-Ming Liu: Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City 106, Taiwan
Chongyang Shi: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 102488, China

IJERPH, 2022, vol. 19, issue 19, 1-17

Abstract: Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human–Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

Keywords: therapeutics devices; therapeutic applications; e-health system (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/19/12857/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/19/12857/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:19:p:12857-:d:935791

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().