Detecting Parkinson disease features with walking activity data

Due to aging of the population, some studies predict that the burden of Parkinson Disease (PD) will grow substantially in future decades. The rapid increase of PD will place a substantial burden on individuals, society, and health systems. In recent years, a series of works have been published on the use of mobile devices, equipped with sensors, such as accelerometers, gyroscopes and magnetometers to diagnosis and monitor PD outpatients . In this work, the influence of a series of factors on the diagnosis of Parkinson disease were evaluated, using walking activity data obtained from an mPower study. Through constructing several databases, the following factors were evaluated: dependent individual and independent individual approach, input record size, interleaved and non-interleaved data. In addition to these factors, the effect of the complexity of the CNN network on its performance was also evaluated. Databases with large records provided models with better performance in PD diagnosis than databases with small records. CNN's complexity also had a great impact on PD diagnosis performance. In this work, the best results achieved for the independent individual approach and for the dependent individual approach were an AUCROC of 0.511 and 0.861, respectively.