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.
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.