Recently I have been playing around with and implementing Mongodb aggregation queries in order to power the qcDashboard analytics and graphs. Although Meteor doesn’t support Mongodb’s aggregation framework in MiniMongo on the client the Mongodb driver on the server side is based upon the node js driver. Thus some smart packages are available on atmospherejs.com that have exposed the aggregate() function as an extension of the Collection class of meteor which can only be used in server code. So far I have implemented basic server methods that return overall userbase average BMI and and weight. The next step is to be able to filter by postcode of users followed a more generalised method to be able to provide more flexibility in filtering options.
This blog entry is being written whilst on my way to Sheffield for a Wing Chun Kung Fu seminar, which is another big interest in my life like computer science and software development.
A wise man can learn more from a foolish question than a fool can learn from a wise answer
As part of my Computer Science degree at the University of Bradford, in my final year I must take part in an individual project that spans both the semesters of the final year. This is the Final Year Project. This can be either selected from a list of projects proposed by academic staff members and/or industry representatives (for commercial companies, organisations or university departments) or be proposed by the student.
I chose to propose my own project that targets the area of health informatics and the quantified self movement that is currently being fuelled by apps such as MyFitnessPal and JawBone, FitBit et al.
My project proposes to design and implement a proof-of-concept platform consisting of a mobile application for the recording of metrics such as weight, daily activity, blood pressure and blood sugar (these are the metrics I have selected for the purpose of the proof-of-concept, although I endeavour may be added if required in the future). The progress of a users health using these metrics will be provided graphically. Additionally a dashboard web app will be implemented that takes aggregate data provided by mobile app users to present graphically and tabularly to mobile app users but mainly to public health organisations and health professionals such as pharmacists, nurses, GP’s/Doctors and other people in the healthcare sector. This could possibly aid the latter groups profession and decision making processes through filtering of data using parameters such as area, age etc. Live up to date information providing anonymised and aggregated health data as an Open API will also help fuel the open data phenomenon evident at current times.
In order to provide myself with some tool to allow me to reflect on the research, design and development of the project and also to allow me to log my progress in this project I have decided to use this blog as a sort of log to track my progress. Additionally this blog will also help me to kickstart intention to start blogging.