BIOTECH

Proyect in collaboration with:

– IEEE SIGHT Argentina Members:

• D. López De Luise
• M. F. Agüero
• C. Párraga
• R. Azor

– TFS. Burgos y Valencia:

• María Fernández
• José Luis Cuesta
• Luis Toriego

– Polibienestar.Valencia:

• Jordi Garces Ferrer
• Estrella Durá Ferrandis
• Claudia Grau Rubio

– Dimper:

• José María Cámara
• Pedro Sánchez
• César Represa
• José Gómez
• Beatriz Núñez
• Rosa. M. Santamaría
• José Antonio González

– Burgos:

• Eduardo Navarro

– Puebla:

• Carlos González
• Claudia Prieto
• Erik Rosales
• Gidy Rosales

– Universidad de Mendoza
– Universidad Nacional de San Juan
– TIPNEA
– Centro CIEL
– IEEE Argentina

Goals

To organize an interdisciplinary team with professionals and scientists from healthcare and engineering focused on Autism.
Design electronic devices and software systems to create a meta-language and model linguistic reasoning of autistic patients. The team will use the Morphosyntactic Wavelets.
Design advanced treatments and diagnosis techniques for Autism, based on software and hardware solutions.
Our goal is to help people with ASD and improve their quality of life

Motivation

Autism (ASD) is increasing every year all over the world, in extremely polluted regions. In the USA, 1 out of 54 boys and 1 in 252 girls are diagnosed with Autism. ASD is estimated to affect more than 2 million individuals. In Argentina, 100,456 children between the ages of 0 and 14 years old have ASD.

Activities

The team is organized into 5 groups

MLW (morphosyntactic linguistic wavelets) Goal: apply linguistic reasoning model with and interfaces.

Audio devices (GDA)Goal: design and develop prototypes do hardware and software devices (stand-alone/WEB) to enhance patient communication.

Visual devices (GDV)
Goal: define and develop prototypes for software/hardware devices to enhance visual interaction with the patient.

Metrics/Indicators (GMI)
 Goal: define a set of metrics and indicators to evaluate properly data from technologies.

Metalanguage inferences (GIM)
 Goal: Design and test a new meta-language from indicators and metrics collected from devices. This meta-language is evaluated by a morphosyntactic wavelets model to reproduce linguistic reasoning and detect anomalies.