The TIMBRE project aims to optimize the T-ROOM technological solution for the diagnosis of Autism Spectrum Disorder (ASD) by incorporating new psychophysiological measures such as eye-tracking and a biomarker detection layer for this disorder.
Autism spectrum disorder (ASD) is a lifelong heterogeneous neurodevelopmental disorder with an estimated global prevalence of 1 in 160 children, according to the World Health Organization (WHO, 2019). In Europe, it was estimated to be around 5 out of 100 children. The DSM-V and the ICD-11 of the
World Health Organization are the two international standard classification manuals that provide the criteria and categories for the diagnosis of ASD (APA, 2013; WHO, 2019). According to the DMV, core symptoms include impairments in communication and social interaction, behaviours repetitive and restricted behaviours and unusual sensory interests or sensitivity.
TIMBRE is therefore a pioneering project:
1) in the use of biomarkers with virtual reality, since to date only studies have been carried out with videos or controlled environments and
2) in the use of machine learning to obtain behavioural biomarkers for the diagnosis of TEA objectively, using advanced signal processing techniques based on cognitive computing such as machine learning and neurocomputing.