airLert

Develop an app or platform to crowd-source information for comparing changes in environmental factors, such as temperature, relative humidity, air pollution, with occurrence of symptoms of allergies and respiratory diseases. Create tools for public entry and grading of symptoms, including but not limited to cough, shortness of breath, wheezing, sneezing, nasal obstruction, itchy eyes; and geographic mapping of symptom frequency and intensity. Create a platform for comparison of symptom map with NASA provided data, with visualization options for web and/or smart phone.

AirLert is a project aimed at decentralising the process of air pollution data collection to the street level, and to effectively use this data to create a direct health impacts on an individual level, for anyone living in an urban environment.

Our app essentially provides users with information on safe/healthy pathways through the city. The app is also capable of obtaining information from the user regarding their personal health. It then uses it to intelligently warn the user of pollution levels that could potentially cause an asthma attack and other respiratory troubles that can often be life-threatening. Alternatively, the app also allows users to input health reactions/symptoms to air pollution, the app then correlates this with the current air pollution data and intelligently learns your personal effects to certain air pollutant levels to warn you in the future.

The app also allows health enthusiasts to choose and pick healthy routes across cities for exercising.

The idea also has a tremendous applicability for business interests. As more and more people are getting increasingly serious about personalising health monitoring through products like fitbit, nike fuel band and other wearable devices, allowing the option to choose healthy running or cycling spots through the city would be of great interest.

Furthermore, the idea also helps to promote STEM learning. As Raspberry Pi’s cost about 4 pounds, kits can be built by students and placed in their university/school or household backyards. They can then use our app platform to learn to code to integrate sensors and learn to analyse air pollution and it’s effect on health in greater detail.

The hardware consists of a Raspberry Pi connected to basic air pollution sensors, which is then subsequently mounted on the tops of busses. The busses act as a mobile unit of data collection all through the city. As the busses move across set routes through the city, this provides us with a robust data set that is updated several times during the day. The busses also cover areas that are most populated by humans in a city, allowing us to obtain data that has a direct impact to urban dwellers. Once the data is received, our application acts as an innovative platform to convert this robust data to direct human health impact.

The air sensors we used included - Carbon Monoxide Sensor, Air Quality sensor, Humidity and GPS sensor.

To build the mobile site, we used CSS, HTML, JavaScript, Node.js, Python (for the hardware) and a web server to host the website. The data from the Raspberry Pi would get sent to the server and the web page would update accordingly.