Smart sensors for a wearable-free and contactless virtual ward at home

Lead Research Organisation: Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science

Abstract

The project will support initiatives on maintaining independence at home, and health within the home. To do this, the consortium will explore the feasibility of using a suite of minimally intrusive, wearable-free and contactless sensors, to create an easy-to-deploy monitoring system for patients at home and in care environments.
Home monitoring using an extended/virtual ward has proven to be an effective solution to challenges during the pandemic in 2020-21 (https://www.england.nhs.uk/nhs-at-home/covid-virtual-wards/). Virtual wards accelerate discharge from hospitals to homes and residential environments, by providing remote patient monitoring for clinicians. The accelerated discharge has numerous benefits: reduced risk of infection, reduction in decompensation (a condition which leads to longer hospital stays and poorer outcomes), and an increase in hospital bed capacity. Existing approaches have used a combination of physical measurement devices (e.g. pulse oximeters) and telephone services to manage patients at home and identify deterioration early. They have been most effective for patient cohorts where there are other carers/family members at home and where patients/carers are younger and have a high level of health and technology literacy.
The core sensor technology is based on millimetre-wave (mm-wave) radar, which is used to look for movements and signs of activity without the use of invasive cameras or intrusive pendants/wearables. Artificial intelligence is used to interpret the outputs of the radar, to create a picture of residents' activities and recognise whether: they are getting out of bed, walking across a room, sleeping soundly, or if they have potentially fallen over. It can also be used to measure heart rate and respiration rate. The primary mm-wave sensor is used in conjunction with an IR camera for contactless temperature and pulse oximetry measurement, and a further suite of sensors will support these tasks by measuring the state of the care environment (temperature, air quality, etc.). Time series algorithms and AI techniques will be used to interpret patterns and search for anomalies within the sensor data, in order to identify health deterioration. As an example, the time it takes a person to get up from bed and walk to the bathroom or kitchen can be monitored over time, to report on whether their mobility is degrading or improving.
Funding from the project will be used to test with focus groups of patients and clinicians in a homecare environment (ExtraCare): the attractiveness of this type of home monitoring, the technologies which are easiest to use and the design of the interface. This will go beyond the AI code of conduct. The technologies underpinning the mm-wave sensors will be further enhanced to improve activity recognition and vital signs detection AI models, with forecasting models (such as recurrent neural networks) extended to predict patient health changes based on sensor inputs. Funding will also be used to develop the interfaces needed to integrate the sensors, evaluate the contactless sensors in comparison with standard health monitoring sensors (AHSN as an evaluation partner), and engage with stakeholders from the local authority, NHS, and care communities.

Publications

10 25 50
 
Description Evaluate remote healthcare monitoring with the Health Innovation Network (NHS) 
Organisation Health Innovation Network South London
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution The research team has provided insights into the potential of home monitoring, elucidating the types of data that can be collected and offering guidance on its interpretation using AI algorithms. Through leveraging our expertise in radar, wireless sensing technologies, and health informatics, the team have helped inform HIN's understanding of cutting-edge ambient sensing technology, empowering them to explore innovative solutions for the improvement of healthcare. Similarly, through HIN-arranged interviews with healthcare experts, the team have explored how radar-based sensing can be used for real-time, continuous monitoring of: falls and falls risk prediction; sleep quality; and vital signs tracking. Health Innovation Network is an NHS team that works flexibly across the health and care sector, industry, academia and in partnership with south London residents to speed up innovation and improve care. They advise, support, and connect teams to successfully adopt innovation, so they are very well placed to accelerate adoption of our research outcomes.
Collaborator Contribution HIN has been heavily involved in the Patient and Public Involvement and Engagement (PPIE) component of the project. They have: served as an advisory on clinical and diagnostic use of remote monitoring technologies and adoption by the NHS/care service; facilitated connections with experts in falls/mobility (such as physiotherapists), vital signs (such as carers/nurses/specialists such as cardiologists), and sleep (both clinical and researchers); refined the user-centric aspects of our project by advising on how clinicians, carers and users might interact with remote monitoring health technologies (both hardware and software).
Impact Output 1: PPIE1 at ExtraCare Engagement with healthcare professionals, users/patients, and facility staff in two stages. First stage: a group including 5 NHS staff, 8 ExtraCare (retirement village) residents, and 5 ExtraCare staff members (carers). Second stage: 7 ExtraCare residents, 6 ExtraCare staff members. Output 2: PPIE2 with Healthcare Professionals & Field Experts The second PPIE of our project involves perspectives of field experts with different backgrounds, experienced in various healthcare settings. Engaged with 5 falls experts, 6 vital signs experts, and 5 sleep experts in part one. This is ongoing.
Start Year 2022
 
Description Extracare residents engagement 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Study participants or study members
Results and Impact Over a series of information sessions, held at the Extracare Charitable Trust's 'Street Meetings', project team members engaged with approximately 60 elderly residents, staff, and carers, on the topic of ambient sensing technologies for remote monitoring of health and wellbeing in the home.
The information sessions served to inform them of the capabilities of these technologies, and as a means to recruit residents and staff for focus groups and for involvement in the project. 20 residents were recruited as volunteers to have ambient sensing technologies installed in their apartments.
Year(s) Of Engagement Activity 2022,2023,2024
 
Description PPIE at the Extracare retirement village 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Patients, carers and/or patient groups
Results and Impact Engagement with older adult residents at ExtraCare, ExtraCare staff, NHS clinicians and other health care professionals to identify the key issues that are being faced in monitoring older adults' health and wellbeing, and their thoughts and opinions on different ambient monitoring technologies.

PPIE Project 1A: engaged with 5 NHS Staff, 8 ExtraCare residents, 5 ExtraCare staff members.
PPIE Project 1B: 7 ExtraCare residents, 6 ExtraCare staff members.

Findings indicated that residents had fewer concerns than staff members, and were more in favour of non-invasive (ambient) sensors than existing wearables for monitoring their behaviour. Older adult participants found the additional monitoring would make them feel more secure, and it was more convenient than pressing a button (e.g. with fall pendants). Staff members were concerned about the possibility of having extra information to monitor.
Year(s) Of Engagement Activity 2022,2023
 
Description PPIE2 with Healthcare Professionals & Field Experts 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact This second PPIE partof our project involved detailed consultations with experts in three critical areas: falls (including detection and risk monitoring), vital signs, and sleep. Our aim was to seek their professional insights on the potential utility of real-time and continuous monitoring to support the health and well-being of older adults, and how such information could enhance their daily practice.

In discussions with falls experts, there was a unanimous recognition of the substantial value that this technology could bring to their practice. The prospect of creating prediction models and establishing continuous screening for falls was particularly appealing. Experts in this domain expressed enthusiasm about the potential to improve patient outcomes by leveraging the data obtained through the monitoring technologies, ultimately contributing to a proactive approach in fall prevention.

Experts specialising in vital signs expressed reservations regarding the accuracy of the monitoring technologies. Despite this concern, they acknowledged the potential benefits of having a baseline for vital signs in real-time. Recognising the potential for continuous monitoring to offer valuable insights, these experts emphasized the need for further validation and refinement of the technology to meet the rigorous standards required for healthcare applications.

In discussions with sleep experts, a consensus emerged on the significant value that continuous monitoring could bring to the field. Traditional methods such as polysomnography, while highly effective, are prohibitively costly and require patients to undergo sleep studies in a laboratory setting. The prospect of replacing or supplementing these studies with in-home monitoring technologies was particularly appealing. Sleep experts saw the potential for increased accessibility, reduced burden on patients, and the ability to gather valuable data in a more natural environment, contributing to a more comprehensive understanding of sleep patterns. As this is considered a nascent field of research, but with significant impact on health, the discussions have resulted in promising avenues for follow-on research projects.
Year(s) Of Engagement Activity 2023,2024