Human activity recognition is attracting interest from researchers and developers in recent years due to its immense applications in wide area of human endeavors. The main issue in human behaviour modeling is represented by the diverse nature of human activities and the nature in which they are performed by the individual makes them challenging to recognize. In this paper we propose a method aimed to recognize human activities and detect users using features gathered from accelerometer sensors widespread in wearable and mobile devices. We exploit machine learning aimed to build models with the ability to discriminate between a set of user activities: sitting, sitting down, standing, standing up and walking. Furthermore, we demonstrate that the proposed method is able to distinguish between different users and to identify the user genre. Real-world experiment shows the effectiveness of the proposed solution.
|Titolo:||Wearable Devices for Human Activity Recognition and User Detection|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|