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.
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 moreParkinson’s disease is a neurological condition that affects motor control and movement.
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.
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.
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