WearAQ explores how school children make sense of the complex issues around air quality. It considers how they might combine their innate subjective perception and intuition with wearable technology and machine learning algorithms to investigate air quality issues.The team worked with students at the Marner Primary school to go out into the surrounding neighbourhood, measure air quality both technologically and through their own perceptions, and recorded their subjective experience using low-tech wearable devices that catalogued their gestures. This data was compared with measurements from expensive, highly calibrated pollution monitoring equipment and other data like temperature, wind and humidity to look at correlations and contrasts.
“This experiment demonstrates the possibility for people to be the ‘sensors’ in measuring air quality, inverting the conventional passive approach of using machines to collect air quality data and citizens as passive receivers of information. Instead, we consider citizens as active participants in collecting data, providing their opinions, and deciding as a community, as a group and even as individuals about what needs to be done regarding the issue.”
Close-up of the final wearable device fitted on a hand and the 10 wearable devices, phones and airbeam device. Each wearable device consisted of an Arduino-compatible microcontroller on an adjustable wristband and a sensor attached to a glove, which read and detected hand gestures.
Students learning the three hand gestures.
Students performing the body gestures during the exploration walk.