NodeNs and QMUL win prestigious Innovate UK grant award
NodeNs and QMUL have collaborated to successfully bid for an Innovate UK grant on Millimetre-wave AI-enabled Smart Healthcare monitoring.
“NHS hospitals are experiencing widespread shortages in bed space and front-line staff time, contributing to long waiting times and budget shortages. This is largely because demand for services is outstripping growth. Improved efficiencies and patient flow could release up to £900M per annum in savings, save staff time, and free up facilities. The NHS is tackling this through pilots of real-time location sensing (RTLS) technology, which seeks to track patients, staff and equipment in real-time. This has many potential benefits such as: finding equipment more quickly, identifying available bed space, and monitoring patient-staff contact time. However, currently existing product solutions suffer from a combination of: relatively poor tracking accuracy and reliability, expensive and bulky tags (unpopular with staff and patients), and high infrastructure costs. The project seeks to develop an artificial intelligence (AI)-based millimetrewave sensing technology to provide industry-leading tracking resolution, at low costs and ease-of-use.
The millimetrewave (mmWave) regime (approximately 30-300 GHz) encompasses the band of the electromagnetic spectrum between microwaves (Wi-Fi, mobile phones) and infrared. Due to recent technological breakthroughs, mmWave electronics will in coming years be used for automotive radar, next-gen Wi-Fi (WiGig) and mobile communications (5G). The technology also allows us to sense objects with extremely high precision, and detect movements of less than a millimetre. This could include wireless detection of heart beats, as well as fall detection or patient tracking, all done without necessitating any worn tags. While our current prototype sensor is extremely accurate, it does not have the ‘intelligence’ to process all the data it receives, and can have difficulty distinguishing between different objects; so it may not be able to tell between a patient and a bed, for example.
To overcome this shortcoming, NodeNs Medical proposes to use state-of-the-art AI algorithms to give the sensor ‘smart’ capabilities, allowing it to learn to distinguish between different detected objects. This could be done by recognising the periodic rhythm of a heart beat, or a person’s swinging arm or leg.
NodeNs Medical will develop a ‘smart’ AI-enabled mmWave RTLS solution which will:
1. track patients, staff and equipment, to identify if staff have visited patients or to track infection spreading,
2. monitor for fall detection,
3. track vital signs.
The device will have market-leading accuracy and ‘plug-and-play’ capabilities for easy installation, helping the NHS to improve patient outcomes while lowering costs.”