A wearable uses AI to monitor asthma symptoms and predict triggers, helping improve outcomes
Spotted: Design engineering student Anna Bernbaum won a runner-up Dyson Award for her wearable device that can alert asthma sufferers to an upcoming attack. The device, dubbed Afflo, works by analysing the users’ respiration and the environment to determine possible triggers for an attack.
Afflo uses a specialised microphone to collect respiratory audio signals. The microphone is placed on the chest each day, using adhesive disks. Environmental information is also collected, using sensors worn in a backpack or belt loop. The two streams of data are then analysed using a machine learning algorithm and the results are sent to the user via a mobile app.
The goal is that once users know the type of situations and environments that tend to trigger asthma attacks or difficulty in breathing, they can take steps to limit their exposure. Data can also be reviewed remotely by medical professionals, allowing them to refine treatment plans more cost-effectively.
The prototype uses machine learning to differentiate between a cough and speech, correctly identifying a cough (a sign of asthma) with 82 per cent accuracy. Both the wearable and the sensor can pair with the patient’s phone via Bluetooth to transfer data.
Afflo is one of many innovations that are using machine learning to improve outcomes for patients. Other recent innovations include glasses powered by AI that help autistic children read facial expressions and an app that can detect early warning signs of Parkinson’s.