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

用于家庭免穿戴式非接触式虚拟病房的智能传感器

• 批准号: EP/W03199X/1
• 负责人: Khalid Rajab
• 金额: $ 50.46万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW03199X%2F1
• 关键词: Smart; sensors; wearable; free; contactless

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.

该项目将支持关于保持家庭独立和家庭健康的倡议。为此，该联盟将探索使用一套侵入性最低、无穿戴和非接触式传感器的可行性，为家庭和护理环境中的患者创建一个易于部署的监测系统。事实证明，使用扩展╱虚拟病房进行家居监控是应对2020-21年疫情期间挑战的有效解决方案（https：//www.england.nhs.uk/nhs-at-home/covid-virtual-wards/）。虚拟病房通过为临床医生提供远程患者监控，加快了从医院到家庭和住宅环境的出院速度。加速出院有许多好处：降低感染风险，减少失代偿（导致住院时间更长和预后更差的情况），增加医院病床容量。现有的方法已经使用物理测量设备（例如脉搏血氧计）和电话服务的组合来管理在家的患者并早期识别恶化。对于家中有其他护理人员/家庭成员的患者群体，以及患者/护理人员较年轻且具有较高的健康和技术素养的患者群体，它们最有效。核心传感器技术基于毫米波（mm-wave）雷达，用于寻找运动和活动迹象，而无需使用侵入式摄像头或侵入式吊坠/可穿戴设备。人工智能被用来解释雷达的输出，创建居民活动的图片，并识别他们是否起床，穿过房间，睡得很香，或者他们是否可能摔倒。它还可以用来测量心率和呼吸率。主毫米波传感器与红外摄像头结合使用，用于非接触式温度和脉搏血氧测量，另一套传感器将通过测量护理环境的状态（温度、空气质量等）来支持这些任务。时间序列算法和人工智能技术将用于解释模式并搜索传感器数据中的异常，以识别健康恶化。例如，可以随着时间的推移监测一个人从床上起床并走到浴室或厨房所花费的时间，以报告他们的移动性是降低还是改善。该项目的资金将用于在家庭护理环境（ExtraCare）中对患者和临床医生的焦点群体进行测试：这种类型的家庭监测的吸引力、最容易使用的技术以及界面的设计。这将超越AI行为准则。支持毫米波传感器的技术将进一步增强，以改善活动识别和生命体征检测AI模型，预测模型（如递归神经网络）将扩展到基于传感器输入预测患者健康变化。资金还将用于开发集成传感器所需的接口，与标准健康监测传感器（AHSN作为评估合作伙伴）相比，评估非接触式传感器，并与地方当局，NHS和护理社区的利益相关者进行接触。

项目成果

A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier

• DOI: 10.1109/jsen.2022.3198395
• 发表时间: 2022-10
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Zheqi Yu;Ahmad Taha;William Taylor;A. Zahid;Khalid Rajab;H. Heidari;M. Imran;Q. Abbasi