Recipient organisationCardiff UniversitySource-published name: Cardiff University
Funding£1.6M
PeriodMay 2024 — May 2028
In plain English
AI plain-English summary
Tiny head-mounted cameras will capture what toddlers with Down syndrome see and hear during their first two years at home, revealing how attention and movement difficulties shape their everyday learning. Most child development research tests children in controlled lab settings with screen-based tasks, which tells scientists what children *can* do but not what they *actually do* in real life. For children with conditions like Down syndrome, ADHD, or autism—where attention and motor problems often appear before a formal diagnosis—this gap is significant. Families and therapists have no clear evidence on how these difficulties affect learning during ordinary moments like mealtimes or play. If successful, the project will produce two concrete outputs: a detailed picture of how attentional and motor constraints alter a child’s visual and auditory experiences, and machine-learning tools that automatically analyse video footage. Currently, extracting useful data from head-camera recordings requires hundreds of hours of manual coding, which blocks wider use in clinics and homes. Automating this analysis could let speech therapists, physiotherapists, and researchers quickly assess a child’s learning environment without specialised training. The final phase works directly with Down Syndrome Education International to turn these insights into practical changes in early intervention programmes.
View original technical description
When children are young, a lot of learning happens as they move around and look at objects and people at home. Families and professionals working with children often worry that the difficulties many young children have with focusing their attention and moving their bodies affect how they learn. These difficulties are surprisingly widespread in young children who develop differently (including children with Down syndrome, ADHD, and autism). In fact, these difficulties are often the first flags for these differences. This Fellowship will help us to find better ways of understanding the needs of young children with attentional and motor difficulties and supporting their learning. At the moment, little is known about how attentional and motor difficulties affect everyday learning. The reason we know so little is because scientists mostly study young children by sitting them in front of a screen to capture their eye-movements (screen-based eye-tracker) or giving them some new tasks to do, so different children can be carefully compared. This way of studying child development has taught us a lot about what children CAN DO, but very little about what they are ACTUALLY DOING in their everyday lives. Fortunately, there have been some exciting technological developments that allow us to step into the shoes of children and see what they see, and hear what they hear. By putting small light-weight cameras on children's heads, we have an opportunity for the very first time to understand what young children with attentional and motor difficulties are experiencing. This can teach us a lot about their everyday learning at home. And this is exactly what we aim to do in this Fellowship. Firstly, we will use the special cameras with young children with Down syndrome. This is a group of children who we know are likely to be developing differently. A lot of these children show attentional and motor difficulties from birth. We will follow these children across the first two years of life when attentional and motor abilities develop quickly. We will then analyse what children with difficulties in these abilities see and hear and how it differs from children without these difficulties. This will help us to think about how we can improve opportunities for learning in children with attentional and motor difficulties. Secondly, we will tackle the main barrier to using special cameras with a lot of children across different settings (including interventions), which is that it takes a huge amount of time and training to extract the information we need from video footage. We will overcome this barrier by using another recent technological advancement: machine learning. We can now train computers to help with some of the time-consuming work carried out by humans. We can use the videos we analysed during the first part of the Fellowship for this training. Our goal is to create tools that help everyone (e.g., other researchers, speech and language therapists, physiotherapists) to learn about the everyday experiences of children. To make sure the tools are made in a way that other people can easily use them, we will involve some of these people in designing the tools with us. Thirdly, we will work with families and professionals delivering interventions to better understand how the interventions are delivered and what the best and most realistic way would be to use the knowledge we have gained and the new tools we have developed to make a difference. As part of this process, we will spend time working with a charity (Down Syndrome Education International [DSEI]) which has a lot of experience with using what researchers learn to improve children's lives. It will take us four years to complete the first three parts of the Fellowship (Phase 1, Year 1-4). But it will leave us in a strong position to actually improve the interventions young children are receiving (Phase 2, Year 5-7), which will improve their development and quality of life.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know