Title :
Toward Collective Intelligence for Fighting Obesity
Author :
Addo, Ivor D. ; Ahamed, S.I. ; Chu, Winnie C.
Author_Institution :
Marquette Univ., Milwaukee, WI, USA
Abstract :
The emergent prevalence of childhood and adolescent obesity remains one of the most significant health care challenges facing the United States today. On the other hand, breakthroughs in Human-Robot Interaction (HRI) research and the diminishing cost of personal robots and virtual agents along with the ever-increasing use of smart personal devices, suggests that there is room for harnessing the power of ubiquitous intelligent systems that can work in partnership to solve some of our most difficult challenges in the very near future. In this paper, we present the design and prototype implementation of a collective intelligence approach aimed at employing machine learning algorithms that work in concert to facilitate the personalization of a humanoid robot Health Coach with a focus on childhood obesity intervention through Child-Robot Interactions and other adaptive Ubiquitous Computing (UbiComp) solutions.
Keywords :
health care; human-robot interaction; humanoid robots; medical robotics; ubiquitous computing; HRI; United States; adaptive ubiquitous computing; adolescent obesity; child-robot interactions; childhood obesity; collective intelligence approach; health care challenges; human-robot interaction; machine learning algorithms; personal robots; robot; ubiquitous intelligent systems; virtual agents; Humanoid robots; Obesity; Pediatrics; Prototypes; Robot sensing systems; Virtual environments; Collective Intelligence; HRI; Machine Learning; Mobile Computing; Spoken Language Understanding (SLU); Ubiquitous Computing; hildhood Obesity;
Conference_Titel :
Computer Software and Applications Conference (COMPSAC), 2013 IEEE 37th Annual
Conference_Location :
Kyoto
DOI :
10.1109/COMPSAC.2013.109