£865k grant awarded to mobility AI project

A UK-based agency, Innovate UK, are pioneering an AI project which aims to help care for people with low mobility. According to Digital Health, £865,000 has been awarded to Cambridge Bio-Augmentation Systems (CBAS) to fund the creation of a machine learning system that analyses the movements of those with low mobility.

Although their use is especially prevalent in the older generation, with stairlifts for the elderly widely available, the AI project aims to look at those who are at risk of falling, as well as those with orthoses. Hoping to improve care for people who are at-risk, the tracking of movements will hopefully allow medical professionals to gain a better understanding of an individual’s needs as well as to monitor their progress.

Since being awarded the grant from Innovate UK, the trials for the project have begun. Although they are estimated to be undertaken until 2020, Co-Founder and CSO of CBAS, Oliver Armitage, has said: “We are so pleased to be taking our AI platform to the next stage and trying it with real patients in the field. Having the support and validation of Innovate UK shows the appetite for AI solutions in healthcare, and the enthusiasm of our partners shows the willingness of the industry to begin incorporating these types of solutions into their products.”

The ability for clinicians to make data-informed decisions about patients’ treatments will be made possible by awarding participants with movement-tracking devices to be analysed by the AI platform created by the CBAS. These results will then be validated by Queen Mary’s Human Performance labs, which has shown to be 96% accurate in its results.

Dr Aleksandra Birn-Jeffery, Lecturer in Bioengineering, from the Queen Mary University of London said: “Not only is it an exciting opportunity to validate new technology that could make such a difference to patient care pathways, but we will be working alongside some incredible companies that are all striving to improve healthcare. This grant also provides the chance to start delving into understanding how we can define whether an individual’s movement pattern is improving to potentially, in the future, further improve patient gait assessment.”