CASE STUDY
Quantifying movement of infants to improve the diagnosis and treatment of abnormal movement disorders
EARLY LIFE
THEME
UNMET NEED
Movement disorders in children, such as cerebral palsy, are typically diagnosed at 18 months of age when there is a noticeable deviation from typical development. However, it is likely that with observation from specialised healthcare professionals, this time frame could be reduced to between 6 and 9 months.
Faster detection and specialised treatment could have a profound impact on the development of children and young people with movement disorders. For health professionals to detect movement disorders early on, long periods of observation would be required by specialist clinicians.
PROJECT CONCEPT / SOLUTION
This project aimed to explore promising methods of movement analysis using machine learning to autonomously categorise the normal movements of children at rest and at play. The team aimed to create a low-cost methodology to monitor high-risk infants, such as those that required neonatal intensive care, in order to monitor their development and provide treatment interventions at the earliest opportunity, if necessary.
The team aimed to utilise an artificial intelligence system to track and classify infant movements and detect normal vs abnormal patterns. This data would be used to develop a smartphone app that allows parents to record their own infant’s movements and share with their healthcare team.
SUPPORT PROVIDED
FUNDING
Our team provided proof of concept funding over a 10 month period. Further funding was secured through funding applications submitted by our Early Life theme lead, Professor Don Sharkey, and his colleagues at the University of Nottingham.
PATIENT AND PUBLIC INVOLVEMENT AND ENGAGEMENT
Parents and young children played a key role in this study and enabled the research team to record and analyse the children’s movements during play and rest.
FUNDING
Proof of concept funding for this project was provided by NIHR Children and Young People MedTech Co-operative (NIHR CYP MedTech, now known as the NIHR HealthTech Research Centre (HRC) in Paediatrics and Child Health).
STAGE OF DEVELOPMENT
BEFORE SUPPORT
BASIC IDEA
CONCEPT DEVELOPMENT
PROTOTYPE DEVELOPMENT
PROTOTYPE VALIDATION
CLINICAL TESTING
REGULATORY APPROVAL
MANUFACTURING
COMMERCIALLY / PUBLICLY AVAILABLE
CURRENT STAGE
BASIC IDEA
CONCEPT DEVELOPMENT
PROTOTYPE DEVELOPMENT
PROTOTYPE VALIDATION
CLINICAL TESTING
REGULATORY APPROVAL
MANUFACTURING
COMMERCIALLY / PUBLICLY AVAILABLE
POTENTIAL IMPACT
The development of a system that can detect abnormal movement patterns in high–risk infants early on, and therefore enable specialists to treat these conditions as soon as possible, has the potential to significantly improve the quality of life of thousands of children and young people.
The impact of this initial proof of concept project is evident in the successful venture to secure further funding and the publication of a research article following the pilot study.
NEXT STEPS
Following the successful proof of concept pilot study, a further £20,000 was secured in August 2023 to take the project forward and explore how this approach could support ex-premature infants.
PARTNERS