Update on Sandpit 2022 funded projects

In 2022 we announced the first major funding outlay for our network where we ran a day long funding sandpit (see link below this article) that was followed up by online discussions and resulted in 6 submissions for funding. After deliberations we decided to fund 3 feasibility studies, these are listed below. These studies are now nearing completion and we look forward to sharing the outcomes with the wider partners at some point soon. If you have any specific questions regarding the studies themselves then feel free to contact your fellow partners who are undertaking the feasibility studies.

1. Sensorization to Premeditate and Attenuate Symptoms in the Management of Spasticity (SPASMS)
   • PI: Dr A Pujari (Univ of Hertfordshire)
   • Partners: Univ of Manchester, Univ of Lancashire, Univ of Leeds, Univ of Kent, Uni of B’ham
   • Research Aim: Can we develop a Proof of Concept (PoC) wearable sensor system to guide the bespoke remote management of individuals with a clinical diagnosis of spasticity?
2. Closed Loop Platform for Endocrine System Management
   • PI: Dr G Cummins (University of B’ham)
   • Partners: Univ of B’ham, University of Kent, Univ of Warwick.
   • Research Aim: The research question for this project is whether interstitial sensing of cortisol and other biomarkers can be cost-effectively performed and fed into a multifactorial mathematical model of the endocrine system to create a closed-loop system that can accurately mimic the natural rhythms and feedback mechanisms of cortisol when used in conjunction with pulsatile drug delivery systems, such as an infusion pump
3. Multimodal Intelligent Neural Decoder for Accessible and empowering mental healthcare (MindD4AccelCare)
   • PI: Dr M Arvaneh (Univ of Sheffield)
   • Partners: Imperial Coll., Univ of Reading, Univ of Bath, Univ of Sheffield.
   • Research Aim: Through a user-centred approach, this project investigates the feasibility of using consumer-grade devices to inform clinical decision making in diagnosing and monitoring MDD. For this purpose, we will use artificial intelligence algorithms to learn from the brain and heart data, collected from wearable consumer-level sensors, while the users with and without MDD symptoms perform gamified cognitive tests.

Bionics Plus Sandpit 2022