Machine learning for vision based patient monitoring

Lead Research Organisation: University of Oxford
Department Name: Engineering Science


Brief description of the context of the research including potential impact
Vision based patient monitoring (VBM) currently encompasses a range of existing technologies such as the remote monitoring of vital signs such as heart and breathing rate [1]. It has already had a positive impact in
many clinical settings, such as mental health care, acute care and assisted living, giving clinicians a better understanding of a patient's physiological state and patients a better night's sleep [2]. More intelligent visual
systems would open up a wider range of useful downstream applications and, ultimately, data which can better inform clinicians.
Aims and Objectives
The "gold standard" tool for sleep monitoring is polysomnography (PSG). However, the data produced by PSG requires interpretation by a trained expert. Additionally, there can often be subjective disagreement between
experts [3]. This work aims to show that a machine learning approach using VBM can achieve a similar level of agreement, or better, than independent experts achieve when analysing sleep.
This work also aims to identify patient activity that is indicative of underlying health concerns. In particular: restlessness, self-harm and excessive exercise are three examples of activity that could be identified through
VBM using a machine learning approach.
One of the key pillars to machine learning success has traditionally been large, annotated datasets. However, in healthcare, data is often scarce, unlabelled and sensitive in nature. I will explore how these challenges can
be overcome, to develop machine learning systems that can be used to tackle the downstream tasks detailed above.
Novelty of the research methodology
Sleep staging/quantification remains an active area of research. No single process can currently claim to provide an objective measure of sleep. Using state-of-the-art computer vision methods in combination with
existing biomarker data, is a novel approach to achieve this.
Alignment to EPSRC's strategies and research areas
The proposed research strongly aligns with the EPSRC areas of 'Artificial intelligence technologies' and
'Medical imaging'.
Any companies or collaborators involved
My research is kindly sponsored by Oxehealth, a vision based patient monitoring company spun out of the
Oxford Institute of Biomedical Engineering in 2012.
[1] M. Villarroel et al., 'Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit', npj
Digital Medicine, vol. 2, no. 1, Art. no. 1, Dec. 2019, doi: 10.1038/s41746-019-0199-5.
[2] H. Lloyd-Jukes, O. J. Gibson, T. Wrench, A. Odunlade, and L. Tarassenko, 'Vision-Based Patient Monitoring and
Management in Mental Health Settings', Journal of Clinical Engineering, vol. 46, no. 1, pp. 36-43, Mar. 2021, doi:
[3] H. Danker-Hopfe et al., 'Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM
standard', Journal of Sleep Research, vol. 18, no. 1, pp. 74-84, 2009, doi: 10.1111/j.1365-2869.2008.00700.x

Planned Impact

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024050/1 30/09/2019 30/03/2028
2416606 Studentship EP/S024050/1 30/09/2020 29/09/2024 Jonathan Carter