Vision-Based Patient Monitoring in Inpatient Mental Health Care

Inpatient mental health services care for people who can no longer be supported at home and need to be admitted to hospital due to severe mental illness. The acute mental health setting is particularly challenging due to the risks of harm to patients and staff. Vision-based patient monitoring is the use of

cameras and computer vision software to support staff in maintaining patient safety by providing physiological measurements, such as pulse and breathing rate, and information about patient behaviour and activity. Vision-based patient monitoring is particularly well-suited to the inpatient mental health setting as contactless monitoring minimises disturbance, discomfort, self-harm risk and

logistical challenges that may be introduced by wearable technologies, without sacrificing the immediacy of the monitoring, which is often vital to support nurses in responding to emergencies quickly. The central aim of the research will be to develop novel vision-based patient monitoring algorithms

that can support healthcare professionals in inpatient mental health care. These algorithms could be in the form of alerts to immediate risks to patient health, such as rapid deterioration of vital signs. Alternatively, algorithms could help with long-term health monitoring of patients, such as screening for

sleep disorders or tracking longer-term changes in patients' mental health or physical health during their stay on a ward. The novelty of the proposed research comes from access to large-scale high-quality datasets and the development of state-of-the-art machine learning algorithms, such as transformers and LLMs, to

analyse multimodal data from those datasets. Access to these data is granted through collaboration with the vision-based patient monitoring company Oxehealth. Oxehealth will provide access to proprietary datasets, under the appropriate research governance, offering a unique opportunity for algorithmic development and evaluation. Additionally, Oxehealth has many contacts with mental

health institutions and professionals across the UK and abroad, and will provide a unique opportunity for collaboration with clinical experts in the field, as well as potentially running new clinical studies where necessary. Oxehealth is also deploying modern accelerated hardware across its sites, allowing

for the use of state-of-the-art machine learning techniques to be used, deployed and validated at a large scale. This DPhil topic aligns well with the EPSRC research themes. Firstly, the research falls well within the "Healthcare Technologies" theme, as the entire focus of the project is on developing novel

technologies for mental healthcare. Particularly relevant is the "UKRI Ageing - Lifelong Health and Wellbeing Programme", as many mental health wards in the UK specialise in providing care for mental health conditions that particularly affect elderly individuals, such as dementia. A second

EPSRC theme that aligns well with the project is the "Artificial Intelligence and Robotics" theme. The scope of the theme states that "[m]any of the challenges in artificial intelligence (AI) and robotics require a multidisciplinary approach [...] in developing technologies to address real world challenges

for society". This research will be by nature interdisciplinary, applying technologies from machine learning in a mental health setting, and will certainly aim to solve real-world challenges in this sector. Oxehealth will provide access to its technology and proprietary datasets, under the appropriate

governance, and may support the research by setting up and running new trials to gather more data. Members of the Oxehealth research team will support Bernardo in his research and provide supervision, alongside the main University supervisor Professor Lionel Tarassenko. Oxehealth is funding this research