AI analysis of data collected by sensors worn by Parkinson's patients can improve monitoring the disease's progression. — Photography evrymmnt/Shutterstock/AFP Relaxnews
A team of researchers at Oxford University is using a system of wearable sensors and machine-learning algorithms to track the progression of Parkinson's disease. It's a method designed to enable a more accurate monitoring of the progression of motor symptoms in a bid to improve diagnoses.
Researchers from the Department of Clinical Neurosciences at Oxford University in England have demonstrated the usefulness of machine learning algorithms (too often summed up over simplistically as "artificial intelligence") in analyzing data from sensors worn by Parkinson's disease patients. Analysis of the data collected during walking and standing tasks shows the progression of the disease's motor symptoms over time, as participants in this study were assessed every three months. The study shows that such a system can considerably improve diagnostic – and prognostic – accuracy.
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