Smoking is the greatest preventable cause of mortality worldwide. In this paper, we present a social experiment where mobile robot equipped with a cigarette detector alert smokers, in particular those smoking close to children. In our research, we compare different methods. In the first case we trained the cigarette detection model using a homemade dataset based on the pre-trained SSD MobileNet detection model. In the second case we analyzed how smokingNet performs applied to our task. Next to distinguish between children and adults, we take advantage of the Cascade classifier and a neural network. Both networks are built to leverage TensorFlow Lite, a mobile-friendly format that enables inference on-device. When a smoking scene is identified, the mobile robot draws near the smoker and issues a warning based on the circumstances.
Psychoeducative social robots for an healthier lifestyle using artificial intelligence: a case-study / Ponzi, Valerio; Russo, Samuele; Bianco, Valerio; Napoli, Christian; Agata, Wajda. - 3118:(2021), pp. 26-33. (Intervento presentato al convegno 2021 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2021 tenutosi a Ita).