Resolving signal complexities for ambulatory monitoring of motor function in Parkinson's disease

Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development of multiple dynamic neural network (DNN) classifiers whose application are governed by a rule-based controller within the Integrated Processing and Understanding of Signals (IPUS) framework. Solutions are described for time-varying occurrences of tremor and dyskinesia, classified at 1 s resolution from surface electromyographic (sEMG) and tri-axial accelerometer (ACC) data acquired from patients with PD. The networks were trained and tested on separate datasets, respectively, while subjects performed unscripted and unconstrained activities in a home-like setting. Performance of the classifiers achieved an overall global error rate of less than 10%.