The thesis work was carried out at the Polytechnic University of Madrid as part of a larger project called NETremor. This project involves the development of a methodology for the daily monitoring of tremor in Parkinson's (PD) and essential tremor (ET) patients, using a smartwatch. Classifying daily living activities (ADL) performed by PD patients and secondly to assess during which activities tremor occurs most are important milestone in evaluating the effectiveness of drug therapy. In this thesis work, the main goal was to build up an algorithm based on artificial intelligence models, starting from inertial movement unit (IMU) signals that has been provided by a previous study to classify ADLs as efficiently as possible, and then to validate it by recording new data using a IMU sensor embedded in a smartwatch [1].