ASD: ML perspective for Individual Performance Evaluation

D. LÓPEZ DE LUISE; M. FERNANDEZ VUELTA; R. AZOR; M. AGÜERO ; C. PÁRRAGA; NATALIA LÓPEZ; PAOLA BUSTAMANTE; M. MARQUEZ; R. BIELLI; D. HISGEN; R. FAIRBAIN; S. PLANES

Soft Computing Applications. Proceedings of the 6th International Workshop Soft Computing Applications (SOFA 2014)

Springer

Año: 2015; p. 1 - 25

There are several approaches to interact with ASD patients. Many of them don´t relay on vocal abilities because of the wide spectrum of symptoms. But taking data from ML perspective may introduce new sources of information for a better interpretation of patient´s behaviour and helps build new parameters to model individual performance. This paper presents some initial findings in that direction based on the automatic evaluation of real cases as a way to build a parameterised description of patients. Preliminary analysis in patients suggest that the individual performance may be compared with the universe of patients, it is possible to elaborate a performance profiling and to model evolution profiles that are present in a predefined universe of patients. The team is currently defining and testing new audio and video protocols to collect additional parameters. From these findings, it is possible to build a parametric reasoning model using MLW.