In a study from Oxford University, 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 .
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.
In the Oxford study, 74 patients with Parkinson’s were monitored for disease progression over a period of 18 months. The participants wore wearables with sensors in different regions of the body: on the chest, at the base of the spine and on each wrist and foot. These sensors — which had gyroscopic and accelerometric capabilities — kept tabs on 122 different physiological measurements, and tracked the patients during walking and postural sway tests. Kinetic data was then analyzed by custom software programs using machine learning.
The sensor data collected by the wearables were compared to standard MDS-UPDRS assessments, which are considered the gold standard in current practice. That traditional test, in this study’s patients “did not capture any change” while the sensor-based analysis “detected a statistically significant progression of the motor symptoms” according to the researchers.
Having more precise data on the progression of Parkinson’s isn’t a cure, of course. But the incorporation of metrics from wearables could help researchers confirm the efficacy of novel treatment options.