Human activity recognition (HAR) is a key research area in Human Computer Interaction (HCI) and plays an important role in people’s daily life, through computational technologies. HAR can be intended as a typical pattern recognition problem or more specifically as a classification problem with the aim to identify a variety of daily activities (ADLs) performing by an individual at a given moment, to maintain healthy lifestyle, help patient rehabilitation and to detect and diagnose automatically precocious illnesses, such as the Parkinson's disease. HAR can be divided into two approaches: vision-based HAR and sensor-based HAR. In this thesis sensor based HAR is used, that allows to collect data extracted from two type of wearable sensors: accelerometer and gyroscope.