Classification of Activities of Daily Living in Subjects with Parkinson’s Disease using Artificial Neural Networks

Parkinson’s disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable information to assess the quality of movement in patients with PD [2]. The present work proposes improving the follow-up and support systems issued by specialists in patients with PD by identifying and adequately classifying the activities of daily living (ADL) carried out by these patients by applying programmed artificial neuronal networks (ANN) in Python language. The proposed supervised learning method allowed the classification of 6 ADLs through 64 signals from inertial sensors that made up the database (dataset) to train a multilayer perceptron (MLP), achieving an accuracy of 99%. Also, by applying dimensionality reduction to dataset, it was possible to reach 80% of accuracy in the classification. These results can potentially help the specialist to reduce costs and achieve a more accurate evaluation, seeking to improve monitoring and quality in the rehabilitation of these patients.