Children with autism and learning difficulties often need substantial support in schools and in later life with communication, emotion regulation and daily living skills. Teaching these children and measuring their progress in education is challenging. Our recent work (ERC grant INTERACT 313398) showed that tracking body motion in children and adults can give a real-world measure of how much people are engaged in a social interaction. In this project, we develop this result into a wearable sensor system to quantify and support children’s learning. In the SocSensor system, each child and teacher wears a sensor on their wrist (like a FitBit) and we track how they move in relation to each other, that is, their social engagement. This continuous monitoring is unobtrusive and is particularly valuable in understanding the behaviour of children with minimal verbal skills who cannot easily express themselves. Teachers and/or researchers can receive a report at the end of the lesson showing how often children were engaged and who interacted with who, which will allow them to monitor the effectiveness of lessons and compare different educational approaches. This will allow them to optimise learning opportunities for these-hard-to-reach children. The domains of educational technology and wearables are growing rapidly but no other product addresses the needs of children with autism and learning difficulties, so the SocSensors system can make a big impact. In this project, we will develop the interface for the SocSensors sensor system, collect validation data in a large autism school and build connections with business partners. This will move SocSensors from a lab prototype to a viable product, as well as showcasing the product to schools, teachers and the autism community. Thus, SocSensors will lead the way in using psychology and wearable computing to support engagement and learning in children with autism + learning difficulties.

Schizophrenia is a devastating disease with high societal costs. However, little is known about the biological mechanisms behind the disorder, a knowledge gap that has stalled the development of new treatments. We recently discovered that truncating mutations in RBM12, an RNA binding protein, are associated with schizophrenia. This finding provides a novel entry point to understanding the disease, but fully exploiting the discovery requires further examination of the function of RBM12. Here I propose to begin that effort by using the zebrafish model system to first, determine the role of RBM12 in brain development; second, discover the role of RBM12 in brain function as assessed by functional connectivity and behavioural assays; and third, identify RBM12’s direct and indirect targets using RNA-seq and iCLIP (individual-nucleotide resolution cross-linking and immunoprecipitation). These studies will aid in illuminating the biological basis of schizophrenia and, ultimately, lead to novel treatments.