Visionrf: vital signal monitoring using radio-frequency technologies – standard if-ri
Dementia is a syndrome in which there is cognitive function deterioration and memory loss. Alzheimer's disease consists or contributes to 60–70% of dementia cases. At present, worldwide around 50 million people have dementia, but nearly 10 million new cases are added every year. The total number of people suffering from dementia is projected to reach 82 million in 2030 and 152 million by 2050 – a 3-fold increase. This puts tremendous pressure on the healthcare system and society as a whole. Equally important, dementia is overwhelming for patients' families and their carers, as they require full-time care and watch. For all these reasons it timely and imperative to develop a low-cost and efficient full-time health monitoring solution.

The goal of this research is to develop an unobtrusive system Suite (ViSionRF) that will be able to capture the vital physiological signals of users (breathing, heart rate, heart beat shape, body position), remotely by using low-power radar, Wi-Fi and RFID signal technologies.  Envision a home with a single remote and unobtrusive device that acts as a stethoscope, heart monitor, irregular breath detector, and posture sensor. Such a home would have the ability to monitor your breathing, your heart (rate and pulse shape), and your position and alert your doctor when an emergency occurs. Such a home would help tremendously impaired citizens (e.g. dementia patients) and their carers.

Unlike traditional patient monitoring systems that require users to ‘wear’ devices and sensors, the proposed system does not require wearing any wearable electronic or on-body sensor. This maximizes mobility and makes the system completely transparent to the user. This is important as dementia sufferers repeatedly forget or decline to ‘wear’ their sensors. The goal will be achieved by developing a hybrid technology that merges Wi-Fi, radar and RFID responses with advanced signal processing algorithms that are further trained using powerful machine learning.