Quantum Imaging for Monitoring of Wellbeing & Disease in Communities

Lead Research Organisation: University of Glasgow
Department Name: School of Engineering


We have identified the home as the place where future transformational healthcare changes will occur with the greatest impact potential. Our vision is that the home of the future will be an environment that has the ability to follow our everyday movements, behaviour and wellness. In this sense, it will become an extension of our physical bodies, providing us with feedback, advice and alerts in the presence of anomalies in the data streams collected by new-generation sensors. The analysis of the data-streams from the sensors will be based on clinically approved models, thus effectively bringing highly trained expertise directly to the living environment.

Remote detection and monitoring of parameters such as gait, macro and micro-movements, blood flow, heart rate and potentially even brain function, when combined with data-driven models, will allow to both monitor health and the onset of non-communicable diseases (NCDs) but also recovery from NCDs or surgery with personalised and continuously updated re-habilitation programmes.
This therefore takes the concept of precision medicine and extends it to our overall physical and mental well-being, with the vision of enabling "precision healthcare" delivered to the home.

The sensors we are proposing are based on new-generation quantum-inspired cameras. These cameras can detect extremely low levels of light, thus rendering their presence in the home completely unobtrusive. The cameras can also detect the arrival time of light at the sensor with very high precision and at very high frame rates. The combination of these features enables the measurement of both macro-movement (in a similar fashion to more common cameras) and micro-movement (not currently possible with current, low-cost or low form-factor cameras). Micro-movement detection is sufficiently precise to capture nanometric variations in skin/body shape and thus directly detect blood flow, monitoring the precise shape and variations of heart beat. Future, very ambitious plans, include extending this capability to the brain. Our cameras can also be combined with RF technology to provide richer data, e.g. Doppler signals directly related to speed of movement.

All these indicators will be fed into machine learning models that monitor, learn and are updated over time and, most importantly, adapt to the individuals inhabiting the home environment. Thus, the systems will quickly adapt and evolve for bespoke individuals, providing precision healthcare monitoring and feedback.

Alongside the engineers and computer scientists working on the sensors and data analysis, our programme involves clinicians who will provide the interpretation models for our data and also partners who will give us access to new-generation intelligent homes inhabited by users who are already beta-testing sensors monitoring for example gross movement.

Planned Impact

Currently, Non-Communicable Diseases (NCDs) pose a major public health challenge, affecting more than 42% of the population. These individuals require significant resource across primary, secondary and community care. Coronary heart disease remains the leading public health problem in the UK in terms of its economic burden in community care with costs outside of clinical treatment that are approaching £8B. Other NCDs are becoming increasingly important with estimates now suggesting that stroke consumes more than 7% of spending in community healthcare. These figures continue to increase as demographic changes accelerate towards an ageing population.

Economic and demographic trends are likely to impact significantly on community healthcare systems over the coming decades, with increasingly dependent, unwell individuals either living alone or within other community care settings. The capacity for sensing and intelligent feedback in such future homes has the potential to enable residents to initiate programmes of professionally validated therapies for a variety of health and wellness issues.

Bringing precision healthcare to the home environment is therefore the main impact expected from our research programme with all the consequences that this will bring associated to improved life expectancy, improved quality of life, reduced pressure on the NHS and consequent improvement of service quality with the redeployment of the financial resources.

Our technology has the ability to assess not only physical health of an individual but also to measure proxies for mental health, social health and happiness/wellness. It will transform rehabilitation strategies and fitness monitoring, either at home or in community settings. If fully realized, the technology may also have a broad applicability within hospital environments. In all of these cases this research programme has the potential to mitigate the increasing health, social care and wellness costs of an ageing population, with growing prevalence of NCDs, not only within the immediate term but also as we approach 2050 and beyond.

Beyond the long-term impact described above, the following impacts are expected from this project in the short term, namely:

1. Advances in scientific knowledge and understanding that maintain the UK leadership position in the development and exploitation of novel engineering and data science solutions to assess health and wellness;

2. Partnerships with healthcare providers to develop cutting edge technology targeted to accelerate translation and enhance health and wellness of patients and individuals.

3. Contributions to the economic competitiveness of the United Kingdom, through the translation and commercialization of scientific knowledge into new technologies, services and products.

4. Trained researchers with a set of multidisciplinary skills in engineering, healthcare technologists and computing science for both academic and non-academic professions.

In summary we aim to generate impact by revolutionising health and care in the community for 2050 using novel, quantum-inspired, imaging methods, transforming homes and healthcare centres into smart-health environments through innovative machine learning and quantum sensor technology that is unobtrusive and data privacy-friendly.


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