App in action Birkbeck College, University of London

A smartphone app that uses deep learning lets people with Parkinson’s disease test their symptoms at home in just 4 minutes. The app could help people monitor the disease’s progression more closely, and uncover how lifestyle factors may affect their symptoms.

“There’s very little understanding as to how Parkinson’s arises, and patients say that every day the condition is different,” says George Roussos at Birkbeck, University of London.

People report symptom changes related to everything from exercise to socialising to diet, but it’s not yet possible to build a solid picture of how these factors interact. “To understand these differences, we need to monitor the condition regularly, in a quick and easy way, over a long period of time,” says Roussos.


People with Parkinson’s usually only see a specialist once or twice a year. This makes it hard to track the disease progression in an individual in detail, and means that side effects of medication such as deterioration of mood can go unnoticed.

Measuring tremors

With their Android app, called CloudUPDRS, Roussos and his colleagues want to make it easier to track symptoms and flag potential problems earlier. Similar to how a clinician would conduct a Parkinson’s severity test, the app includes both self-assessment questions and physical tests using a smartphone’s sensors.

For example, one test measures tremors by asking the user to hold the phone flat in their hand. Another measures gait by getting the user to walk 5 metres in a straight line and back with the phone in their pocket.

The first version of the app directly mimicked the role of a clinician, so the assessment took around 25 minutes. “The reason it takes so long is because it’s hard to make sure that you get enough good data to make the tests reliable. So you have to overcompensate by performing each test for longer than necessary,” says team member Cosmin Stamate.

Stamate added a deep learning feature so that subsequent versions of the app can distinguish between good data, like a measurement of tremors, and bad data, like the smartphone being knocked. If someone performs the wrong action or the smartphone sensor picks up meaningless vibrations, the app simply ignores it.

Having been trained to recognise these differences using data labelled by experts, the system discards bad data with an accuracy of 92.5 per cent. “Then as soon as we’ve registered enough good data, the user is told to stop,” says Stamate.

The app can also personalise assessments to provide a “quick test” option. This measures only three symptoms that are most indicative of an individual’s overall performance and could reduce assessment times to less than 4 minutes. The team will present the work later this month at the International Conference on Pervasive Computing and Communications in Hawaii.

“I have to admit, I’m really impressed with the accuracy,” says Dragana Miljković at the Jožef Stefan Institute in Ljubljana, Slovenia, who uses machine learning to find patterns in Parkinson’s data. This could really help identify important features of Parkinson’s disease, she says.

A clinical trial is now under way using the latest version of the app, which is certified as a medical device by the UK’s Medicines and Healthcare Products Regulatory Agency. Since October, 60 people with Parkinson’s have been using the app to assess their symptoms every 2 hours. Five experienced clinicians have been performing assessments on the same people every two weeks for comparison.

“The overall aim is to empower people with Parkinson’s,” says Stamate. “This app can help them find the things that improve or worsen their symptoms.”