In this paper a robust Fall Detection Algorithm by using a deep learning approach and a low-cost mobile robot equipped with an RGB camera is presented. This method consists of four steps. The first step is the user detection, achieved by a real-time video stream and a Deep Learning approach. Once the user is detected, then its position is estimated in the second step. In the third step, if a fall is detected, a photo is acquired and a pre-registered audio message asks the user how he is. In the last step the photo and the audio captured are sent to a Telegram Bot (TB) in order to alert family members or caregivers. Tests have been performed in a real scenario.
Fall Detection System by Using Ambient Intelligence and Mobile Robots / Ciabattoni, L.; Foresi, G.; Monteriu, A.; Pagnotta, D. Proietti; Tomaiuolo, L.. - ELETTRONICO. - (2018), pp. 130-131. (Intervento presentato al convegno 2018 Zooming Innovation in Consumer Technologies Conference, ZINC 2018 tenutosi a srb nel 2018) [10.1109/ZINC.2018.8448970].
Fall Detection System by Using Ambient Intelligence and Mobile Robots
Ciabattoni, L.;Foresi, G.;Monteriu, A.;Pagnotta, D. Proietti;
2018-01-01
Abstract
In this paper a robust Fall Detection Algorithm by using a deep learning approach and a low-cost mobile robot equipped with an RGB camera is presented. This method consists of four steps. The first step is the user detection, achieved by a real-time video stream and a Deep Learning approach. Once the user is detected, then its position is estimated in the second step. In the third step, if a fall is detected, a photo is acquired and a pre-registered audio message asks the user how he is. In the last step the photo and the audio captured are sent to a Telegram Bot (TB) in order to alert family members or caregivers. Tests have been performed in a real scenario.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
