Parkinson's tremor classification using machine learning

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• receiving accelerometer data characterizing motion of a subject having Parkinson's disease;
• processing the accelerometer data using a tremor classification neural network, in accordance with values of a set of tremor classification neural network parameters, to generate a score distribution over a set of tremor classes.
• wherein the set of tremor classes comprises multiple tremor classes,
• wherein each tremor class in the set of tremor classes corresponds to a respective severity of tremor;
• and
• classifying a tremor of the subject having Parkinson's disease based on the score distribution over the set of tremor classes.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• receiving accelerometer data characterizing motion of a subject having Parkinson's disease;
• processing the accelerometer data using a tremor classification neural network, in accordance with values of a set of tremor classification neural network parameters, to generate a score distribution over a set of tremor classes,
• wherein the set of tremor classes comprises multiple tremor classes,
• wherein each tremor class in the set of tremor classes corresponds to a respective severity of tremor;
• and
• classifying a tremor of the subject having Parkinson's disease based on the score distribution over the set of tremor classes.