Enabling health, independence and wellbeing for psychiatric patients through personalised ambient monitoring (PAM)

十分之一的英国人会在人生的某个阶段遭受致残性焦虑症的折磨，而每年因心理健康问题而损失的工作日达9100万个，这给英国造成的生产力损失和治疗费用高达320亿英镑。除了整个国家的经济负担之外，当然还有个人，他们的家人和朋友的个人成本（www.mind.org.uk）。最近，卫生服务国务部长罗西·温特顿宣布，政府将通过提供药物信息，帮助患有癌症或精神健康问题等长期疾病的人保持独立，并控制他们的疾病。PAM（个性化环境监测）将更进一步，允许患者选择现成的技术来监测他们的活动特征；对行为模式的测量表明人们的心理健康状况。PAM将在一个人的家中使用一组多个独立的传感器，与一个被编程来检测活动特征变化的计算机系统相连接。然后，这些可以用来向病人、他们的家人或他们的医生发出自动警报，从而提供避免衰弱发作的能力。这项初步研究将探讨减少衰弱发作发生率的可行性。这将通过纵向监测来实现，这将允许及早发现健康恶化并防止可避免的住院。这项工作将以南安普顿大学（ISVR和CORMSIS）在该领域的现有工作为基础（其中包括即将向EPSRC提出的应用于康复监测的类似相关技术的建议）。诺丁汉大学的投入将扩展目前在传统和职业健康领域的生理监测工作，同时他们参与EPSRC资助的健康技术评估IMRC将提供一种潜在评估项目真正价值的机制。斯特林大学已经通过MATCH在该领域开展了业务，并拥有一个稳定的中间件平台，但在项目的早期阶段，我们将评估该平台，特别是我们将与EPSRC EQUATOR项目就分布式健康监测演示器中使用的中间件进行联系。调查人员提供医疗设备和传感器、医疗信号处理、通信和软件服务以及运筹学建模方面的技能和经验。这种混合对项目的成功至关重要，并提供了一个有趣和独特的跨学科分组。IDEAS沙坑为形成这种积极的关系提供了一个独特的机会。可交付成果：对医疗保健：-在精神保健背景下对环境监测进行评估-改善该患者群体的健康，独立性和福祉状况-支持与药物的一致性并更好地了解药物；对技术：-能够简单地插入和打开传感器-用于家庭的低成本，坚固的传感器-新型传感器集成到，例如，服装，个人设备（例如，移动电话，MP3播放器），常规运输方式。所有这些都有可能提供有关“活动”的数据。-智能实时算法，从分散和稀疏的数据中识别行为签名。-展示在（可能）与传感器间歇性连接的移动电话上使用巡回网关的潜力。

One in ten of the UK population will suffer a disabling anxiety disorder at some stage in their life whilst 91 million working days are lost per year due to mental health problems and the cost to the country is 32 billion in lost productivity and treatment costs. In addition to this financial burden to the country as a whole there are of course the personal costs to individuals, their family and friends (www.mind.org.uk). Recently Rosie Winterton, the Minister of State for Health Services, announced the Government will help people with long-term conditions such as cancer, or mental health problems, to stay independent and take control of their illness by providing information with medication. PAM (personalised ambient monitoring) will take this even further by allowing patients to select off-the-shelf technology that will monitor their activity signatures ; measurements of behavioural patterns which indicate people's mental health state. PAM will use a set of multiple discreet sensors in a person's home, coupled to a computer system programmed to detect changes in activity signatures. These can then be used to issue automatic alerts to the patient, their family, or their doctor, thus providing the capability to avert debilitating episodes.This pilot study will investigate the feasibility of reducing the incidence of debilitating episodes. This will be achieved by longitudinal monitoring that will permit early detection of deteriorating health and prevent avoidable hospital admissions. This work will build on existing work in this area at the University of Southampton (ISVR and CORMSIS) (that includes an imminent proposal to EPSRC on similar related technology applied to rehabilitation monitoring). University of Nottingham input will extend current work in physiological monitoring in both the conventional and vocational health domains whilst their involvement in the EPSRC funded health technology assessment IMRC will provide a mechanism to potentially assess the project's true value. The University of Stirling already operates in this domain via MATCH and has a stable middleware platform, however at an early stage in this project we will assess the platform, and in particular we will liaise with the EPSRC EQUATOR project regarding use of middleware developed for used in a distributed health monitoring demonstrator.The investigators provide skills and experience across medical devices and sensors, medical signal processing, communications and software services, and Operational Research modelling. This mix is essential to the success of the project, and provides an interesting and distinct interdisciplinary grouping. The IDEAS sandpit has provided a unique opportunity to form such a positive relationship.Deliverables:To healthcare:- An evaluation of ambient monitoring in a mental healthcare context- Improved health, independence and wellbeing status for this patient group- To support concordance with medication and better understanding of the drug(s)To technology:- The ability to simply plug in and turn on a sensor- Low-cost, robust sensors for use in the home- Novel sensors incorporated into, for example, clothing, personal devices (e.g. mobile phones, MP3 players), regular modes of transport. All have the potential to provide data on 'activity'.- Intelligent real-time algorithms to identify behavioural signatures from disparate and sparse data.- Show the potential for a roving gateway on (possibly) a mobile phone working with intermittent connectivity to sensors.