Implementation of Paper-based Sensor into Disposible Masks for Continous Respiratory Monitoring for Telemonitoring

COVID-19是一种基于呼吸系统的流行病。由于呼吸困难是一个重要的症状，没有定量的方法来监测基本工人或最脆弱的个人的早期症状。通过将Spyras技术应用于口罩，我们旨在监测呼吸模式的不适，并提示可能因COVID-19而恶化。预计2020年仅中国的口罩产量就将达到100亿只（赖琳·阿玛拉，2020年）以及使用这些口罩的人接触COVID的高风险-一种通过嵌入在面罩内的呼吸分析来预测可能的传染的手段变得有吸引力。戴口罩在抗击病毒方面的好处是众所周知的，临床实践中戴口罩的一部分，捷克共和国，摩洛哥和纽约等国家正在强制公众佩戴口罩。个人防护设备是一种持续有限的资源。Spyras可以将其高度精确的纸质传感器集成到3层和FFP 2/3呼吸器面罩中，通过更换现有的单向阀，对现有的制造实践的影响最小。可重复使用的电子器件将包含在连接到一次性面罩过滤器和传感器的小模块内。移动的应用程序将连接到面罩中的传感器，提供呼吸分析。这款移动的应用程序不仅可以收集呼吸数据，还可以作为症状跟踪器，在用户输入定性数据时监测咳嗽、呼吸困难和体温。通过远程应用程序收集的数据可以传递到一个安全的云平台，并提供一个交互式数据仪表板，供用户查看他们的口罩佩戴情况，并提供有关过滤器效率和更换新过滤器以保持过滤器效率的时间的信息。在Innovate UK的资助下，Spyras将能够重新部署和开发其平台，从监测体育和医院中个人的呼吸，在工作或外出时佩戴口罩，并在个人健康状况中采取更谨慎的方法。Spyras可以扩展应用程序功能，以提供病毒后支持，例如实施康复指导和措施。这是为了减少限制性措施解除后出现“第二波”感染的可能性，并向个人保证这种全球性事件的后果。此外，这将对COVID-19后人们的康复产生积极影响。从过去的冠状病毒爆发（如SARS和MERS）中可以看出，不仅在ICU住院的患者需要康复后的康复（Chan 2005）。通过“扩展影响”资金扩大资金将大大建立在过去3个月的工作基础上，早期原型通过临床通气测试放置到用户组测试。这将通过与我们选择的供应商合作来实现，从最初的可行性临床试验到可以在用户试验中依赖的设备，生产可扩展的纸质传感器盒。传感器之间的低可变性和提供这些传感器的专业知识将为用户测试提供最佳条件。

COVID-19 is a respiratory based pandemic. With breathlessness being a vital symptom, there is no quantitative method for monitoring essential workers or the most vulnerable individuals for early symptoms. Using Spyras technology incorporated into face masks, we aim to monitor breathing pattern distress and indicate possible deterioration from COVID-19.With the predicted production volumes of face masks reaching 10 Billion in 2020 only in China(Lai Lin Thomala, 2020) and the high risk of exposure of the people using these masks to COVID-19 a means to predict possible contagion through respiratory analysis embedded within the mask becomes attractive. The benefits of wearing face mask in the fight against the virus are understood and part of clinical practice to wear, countries like Czech Republic, Morocco, and New York are making masks mandatory for the public.Personal protective equipment is a continuing limited resource. Spyras can integrate its highly accurate paper sensors into 3-ply and FFP2/3 respirator masks with minimal disruption to existing manufacturing practices by replacing the one way valves that exist. A reusable electronics would be contained within a small module that connects to the disposable mask filter and sensor. A mobile application would connect to the sensor in the mask providing respiratory analytics. This mobile app would not only gather respiratory data but also work as a symptoms tracker to monitor coughs, breathlessness and temperature when the user inputs qualitative data. Data collected through a remote app can be passed to a secure cloud platform and provide an interactive data dashboard for users to view their mask wearing and provide information on the filter efficiency and times to change to new filters to maintain filter efficiency.With funding from Innovate UK, Spyras will be able to redeploy and develop its platform from monitoring the breathing of individuals in sports and hospitals, to face masks at work or outside and take a more measured approach in their individual health profiles.Spyras can extend app features for post virus support, such as implementation of rehabilitation instruction and measures. This is to reduce the possibility of a 'second wave' of infection when restrictive measures are lifted and to assure individuals with the aftermath of such a global event. Moreover, this would positively impact the recovery of people post COVID-19. As seen from coronavirus outbreaks of the past such as SARS and MERS there will be a need for rehabilitation post healing not only for patients who have been hospitalised in ICU(Chan 2005).Extending the funding by the 'Extension for Impact' funding will significantly build on the past 3 months of work where early prototypes were placed through clinical ventilation testing to user group testing. This will be achieved by working with our chosen suppliers to produce a scalable paper sensor cartridge from the initial feasibility clinical trial to a device that can be relied upon in user trials. Having low variability between sensors and expertise in place to supply these sensors, will enable to optimal conditions for user testing.