LAUSR

People with autism spectrum disorder (ASD) manifest great heterogeneity in their atypical sensory behaviors. It is estimated that 95% of people with ASD have a Sensory Process Disorder (SPD). People with ASD feel the need to control what happens in their environment. However, it is inevitable that new situations occur during a person’s daily life. Therefore, it is important to monitor most of the circumstances they face in an attempt to predict the appearance of disorders that end up affecting their behavior. This paper presents the first steps towards the development of a system for knowing the value and effect on the SPD of different biological and environmental parameters. To obtain those variables, two electronic devices have been designed. The first one is an electronic system for capturing environmental variables such as luminosity or humidity, which is portable and mobile. The second electronic device is a soft wearable wristband which gets biological parameters. To know the effect of those variables on the SPD, a complete software platform has been implemented. Both devices upload day-to-day data to a cloud database where the information is stored in timeseries data of different parameters. The system uses the data to learn a personalized model that is designed to manage the SPD of the user. The main novelty is the use of sensor integration, data processing and machine learning techniques to develop a system able to classify the sensory load supported by a user with ASD while performing different activities. The results obtained so far prove the feasibility of the approach.