In a study from Oxford University, researchers found that by using a combination of wearable sensor data and machine learning algorithms the progression of Parkinson’s disease can be monitored more accurately than in traditional clinical observation. Monitoring movement data collected by sensor technology may not only improve predictions about disease progression but also allows for more precise diagnoses.
Parkinson’s disease is a neurological condition that affects motor control and movement. Although there is currently no cure, early intervention can help delay the progression of the disease in patients. Diagnosing and tracking the progression of Parkinson’s disease currently involves a neurologist using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) to assess the patient’s motor symptoms by assigning scores to the performance of specific movements. However, because this is a subjective, human analysis, classification can be inaccurate.
