Cognitive evaluation aims to the examination of cognitive functions like memory, attention, orientation, language, or executive functions (e.g., activity planning and sequencing), in order to discard anomalies in cognitive capabilities. Cognitive impairments are typically associated to senior citizens, who they could be seriously compromised due to dementia and other related processes. Another population group where cognitive evaluations are typically performed are students, because they can suffer from dyslexia, attention deficit disorder or hyperactivity. Thus, to detect these problems, it is necessary to pass a cognitive assessment. Currently, they take place in a controlled and medical environment and they are mainly based on: neuro-psychological tests (e.g. MMSE, King’s figure, Trail Making Test, etc.). Nevertheless, these classic tools suffer several limitations, such as: they require personalized attention of health professionals; their application is usually performed retrospectively when cognitive problems are clearly; they are very dependent of confounding factors (e.g. age, gender, educational level, behavioural, etc.). Thus, there is a need to develop new cognitive evaluation tools, more natural, transparent, continuous and entertained. Among the new trends to overcome the limitations recently mentioned, some researchers have raised the possibility of using casual video games. Thus, we have proposed the following research challenge: to create a device to estimate the cognitive status of a person, from their interaction with casual games, using machine learning techniques. Then, we are going to try to offer an answer and to contribute new knowledge in this matter, especially in the machine learning field. To conclude, the expected outcome of this research will be relevant under a scientific, social and economic perspective.
Sonia Valladares Rodriguez has a Telecommunication Engineering degree (2011) and a Master course in Telecommunication Engineering (2012) from the University of Vigo. Besides, she has worked as a R&D Engineer since 2011 in the Group of Telematic Systems Engineering (GIST) and currently she is doing her PhD studies in the same research group. Finally, her main areas of research are: e-Health, machine learning, data pattern, adapted and interactive interfaces, and semantic technologies.