The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications

The Use of Historical Data in Rule-Based Modelling for Scenarios to Improve Resilience within the Building Stock

Digital documentation has become integral to the preservation, analysis and communication of historical sites. New platforms are now being developed that involve complex 3D models and allow the analysis of spatial data. These include procedural modelling, a technique that enables the rapid development of ‘dynamic’ 3D environments, and generation of simulations for entire cities, resulting in low cost, high resolution 3D city models. Though procedural modelling has been used in the context of archaeology to ‘recreate’ cities at specific historic time points, the use of historical data in the development of rule-based procedural models for current cities has been little explored. Here, we test the extent to which construction age data, historical building regulations and architectural knowledge can be used in the generation of procedural rules, and the level of detail and potential impact that these models may have. Rather than creating an accurate representation of the city, we instead seek to simulate the way in which urban areas are likely to behave under certain conditions, in order to test what-if? planning scenarios. This allows us to explore more flexible ways of digitally ‘creating’ cities, past and present, and to gain insights into underlying ‘rules’ that govern their physical form