Scientists have created an algorithm which can detect a side effect of Parkinson's treatment that causes involuntary jerking movements.
Prolonged exposure to the dopamine replacement drugs can lead to dyskinesia, causing involuntary jerking and spasms of the whole body.
Academics at Edinburgh's Heriot-Watt University have conducted clinical studies that prove their algorithm reliably detects the condition.
They now using their study to develop a new home monitoring device for patients that will help their clinician adapt and improve treatment.
"The problem is that, as Parkinson's disease worsens over time, the dose required to treat the motor features increases, which increases the risk of inducing dyskinesia, or making it more severe and prolonged for patients who already have it," Dr Michael Lones, associate professor of computer science at Heriot-Watt University, said.
"Patients don't see their clinicians that frequently, and medication only changes at regular review periods.
"So it's very difficult for clinicians to know when dyskinesia is occurring.
"A better solution would be a portable device that identifies and monitors dyskinesia while patients are at home and going about day-to-day life, broadcasting data to their clinicians through simple mobile technology."
The motor features of Parkinson's Disease, such as tremor, postural instability and a general slowing of movement, are caused by a lack of dopamine.
Clinicians treat this through replacement drugs such as levodopa, but prolonged exposure to the substances can lead to dyskinesia.
About 90 per cent of patients treated with dopamine replacement drugs over 10 years reported symptoms, but the exact cause of the condition is unknown.
Dr Lones and his team carried out two clinical studies, with 23 Parkinson's Disease patients who had all displayed evidence of dyskinesia.
Three trained clinicians then graded the intensity of the condition shown by them.
"The clinical studies allowed us to capture and mine data about how patients move and used those to build models," Dr Lones said.
"We've demonstrated that our system can reliably detect clinically significant dyskinesia, which is the information clinicians need to adjust a patient's medication and more effectively manage the side effects, which currently reduce the quality of life for a great number of patients."
