Focused on the design of novel algorithms to provide solutions able to incorporate advanced Descriptive, Diagnostic, Predictive, and Prescriptive capabilities to Clinical Decision Support Systems (CDSS).

Currently focused on their application to Cognitive Stimulation and Rehabilitation (see InnobrainPlay&Sing, and Cognitio projects), on Chronicity and Autism Spectrum Disorders (see BioMoCISVA and AMATE projects), and on Oocyte Biology Research (see Eurova Training Network).

Biometrical monitoring of Chronically ill and Support through a Virtual Agent

Initial/final date: 
01 June 2018 to 31 May 2020
Project researchers: 
Main researcher: 
Project type: 
-
Funding Entity: 
EUROSTARS-II
Description: 

Design of a Machine Learning (ML) system that monitors biometric signals of patients with chronical diseases, to detect medical crisis in an early stage while increasing their autonomy. The system is composed by a smart garment that collects the biomedical data and provides feedback, connected with a 3D virtual agent app that interacts with the patient and applies gamification strategies to revert risky situations. A report service for clinicians completes the system.

Referencia: 

CIIP-20181004

Project Status: 
Ongoing
Funding Amount (€): 
110707.00
Research line: 
Machine Learning for Healthcare
Acronym: 
BioMoCISVA
In collaboration with: 
Narada Robotics, Mobile Biometrics, Ohmatex, Fundació Orienta
Geographical scope: 
European
Grant type: 
Competitive
Transferència: 

Playing and Singing for the Recovering Brain: Efficacy of Enriched Social-Motivational Musical Interventions in Stroke Rehabilitation

Initial/final date: 
01 August 2018 to 31 July 2021
Project researchers: 
Main researcher: 
Project type: 
Otros
Funding Entity: 
Fundació Marató TV3
Description: 

A large percentage of chronic stroke patients (CS) show motor deficits and language impairments. These deficits clearly diminish their health-related quality of life, limiting their socio-familiar and working roles. Because their high incidence, one of the greatest social and economic challenges is to develop cost-efficient, easily and widely applicable rehabilitation tools. In this context, music has arisen as a potential neurorehabilitation tool. Two important applications have been proposed: (i) the use of music training to induce motor recovery (Music supported therapy, MST) and (ii) singing-based interventions for language recovery in aphasic patients. Some of their limitations are the intensive and time-consuming requirements and the lack of solid evidence from Randomized Control trials (RCT).

This project aims to overcome these barriers translating these protocols (music playing and singing) into home-based self-training interventions (using tablet-based designs) and evaluating their behavioural and neural efficacy in two carefully designed RCTs. Both interventions have been improved considering new approaches emphasizing the role of social and intrinsic motivation factors in optimizing motor and language recovery. 

This project will have clinical and societal impact providing first-evidences on the effectivity of two new interventions that could be widely used at home for improving motor and language deficits in CS.

Project Status: 
Ongoing
Funding Amount (€): 
119979.00
Phd Students: 
David Sanchez-Pinsach
Research line: 
Machine Learning for Healthcare
Acronym: 
Play&Sing
In collaboration with: 
Institut d'Investigació Biomèdica de Bellvitge (IDIBELL) Cognitive Brain Research Unit, University of Helsinki
Geographical scope: 
International
Grant type: 
Competitive
Transferència: 

INNOBRAIN: New technologies for the innovation in cognitive stimulation and rehabilitation

Initial/final date: 
02 January 2017 to 31 December 2019
Main researcher: 
Project type: 
CC.AA.
Funding Entity: 
GenCat - EU RI3 Funds
Description: 

Artificial Intelligence technologies for Predicted and Personalized Decision Support Systems (PPDSS) for cognitive stimulation and rehabilitation of people with cognitive impairment as a result of neurological or psychiatric diseases, dementia or developmental disorders.

Referencia: 

COMRDI-151-0017

Project Status: 
Ongoing
Funding Amount (€): 
98475.00
Research line: 
Machine Learning for Healthcare
Acronym: 
Innobrain
In collaboration with: 
Fundació Institut Guttmann, Sky&Earth SL, STARLAB Barcelona SL, Fundació Eurecat, Centre de Visió per Computador, Corporació Sanitaria Parc Taulí, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Institut d’Investigació Biomèdica de Girona (IDIBGI), Fundació Salut i Envelliment (FSiE-UAB),
Geographical scope: 
Regional
Grant type: 
Competitive
Transferència: 

ANALISIS TERAPEUTICO DE COMPORTAMIENTO A TRAVES DE TECNICAS DE DATA-MINING, APLICADO AL AUTISMO

Initial/final date: 
15 December 2014 to 31 January 2016
Project researchers: 
Main researcher: 
Project type: 
Plan Nacional
Funding Entity: 
MINETUR - Acción Estratégica de Economía y Sociedad Digital 2014
Description: 

The goal of the project is to build assistant applications targeted to autistic people, their parents and therapists to help autistic people to handle unexpected situations on a daily basis more effectively, hence facilitating their integration in the society. The project will make use of solid know-how on autism and state-of-the-art machine learning, big data techniques, virtual assistants, and biometrical technologies.

Research line: 
Machine Learning for Healthcare
Acronym: 
AMATE
In collaboration with: 
Narada Robotics and Planeta Imaginario
Geographical scope: 
National
Grant type: 
Competitive

Case-Based Reasoning for the optimization of cognitive rehabilitation on TBI

Initial/final date: 
01 January 2013 to 31 December 2015
Main researcher: 
Project type: 
Plan Nacional
Funding Entity: 
TIN2012-38450- C03-03
Description: 

Acquired Brain Injury (ABI) constitutes a major and increasing social and healthcare concern of high diagnostic and therapeutic complexity. Its incidence and survival rate after the initial critical phases makes it a prevalent problem that needs to be addressed. ABI patients frequently suffer from a series of cognitive disorders related to memory, attention, language, or executive functions. Cognitive rehabilitation aims to reduce the impact of the disabling conditions in order to reduce functional limitations and increase patient's autonomy. For the rehabilitation process to be more effective, treatments must be intensive, personalized to the patient's condition and evidence-based; and require constant monitoring. The main goal of the IIIA team is the research on new machine learning/CBR techniques able to propose and revise personalized therapies in real-time.

Phd Students: 
Xavier Ferrer Aran
Tan Hakan Ozaslan
Research line: 
Machine Learning for Healthcare
Acronym: 
COGNITIO
In collaboration with: 
Universidad Politécnica de Madrid, Fundació Privada Institut de Neurorehabilitació Guttmann
Geographical scope: 
National
Grant type: 
Competitive