International Runner-up in the
James Dyson award
Asthma is a respiratory disease affecting 235 million people globally. Asthma causes airways to swell and narrow in response to an external trigger, making breathing difficult. Afflo is an AI enabled wearable device to monitor asthmatic symptoms and environmental factors, making predictions about a patient's key triggers.
1 in 12 people in the UK are currently receiving treatment for asthma
The NHS spends around 1 billion pounds a year treating and caring for people with asthma
0.4 % of asthma sufferers in the UK have access to specialist care
Afflo is an AI enabled wearable device to monitor asthmatic symptoms and environmental factors, taking the guesswork out of trigger diagnosis.
THE AFFLO WEARABLE
Attaches to the chest to listen to breathing, coughing and wheezing using a microphone.
THE AFFLO POD
A bundle of sensors, gathering data about the users' environment, informing trigger predictions.
THE AFFLO APP
A mobile interface that presents the collected data back to the user.
The data collected by the Afflo Wearable and Afflo Pod is uploaded to the mobile application once a day via bluetooth. Overnight, this data is sent to the Afflo servers to train that user's customised algorithm.
The boxes shown in purple were selected as the main project scope. This allowed time to be focussed on the most critical areas.
To record the respiratory noises made by the user, a microphone would be used. This choice was based on extensive research. The optimal type of microphone and the influence of external factors was found through rigorous testing.
A test procedure was developed to make quick, direct comparison between different microphones and housings.
The Test Cycle
The Breathing Cycle
Chest positioning was tested alongside the effect of clothes brushing past the microphone.
The collected Audio signals were analysed using Python scripts on Jupyter notebook.
Some signals recorded the heartbeat as well as the respiratory signals, disrupting results. This was removed through outlier identification using a dynamic range gate, in which 0.045 seconds of signal was removed from either side of an identified heartbeat.
It was found that the dynamic microphone was the optimal microphone, displaying sufficient sensitivity with minimum power consumption. The effects of clothes movement could be mitigated using a layer of acoustic foam and rubber.
The testing lead to the selection of the most promising microphone option. The Afflo wearable was then designed to house this microphone and other components.
Basic current draw tests using an Arduino were carried out alongside duty cycle calculations to select the optimum battery.
The amount of memory required was found to be 397 MB using the equations below.