Abstract. As described in this paper, we propose a supervisory system that considers actual situations and social aspects of users in a ubiquitous computing environment. To realize gentle and safe supervision while providing efficient supervisory services, the system must recognize the situations of a watched person, such as the person's physical condition. To achieve this, we have proposed a ubiquitous supervisory system "uEyes", which introduces Social Context Awareness: a distinguishing feature for supervision. Using this feature, the system can combine environmental information acquired from sensors in the real world and common-sense knowledge related to human activities in daily life. As described in this paper, we specifically examine design of Social Context Awareness using ontology technologies. Based on this advanced feature, a live video streaming system is configured autonomously depending on the users' circumstances in runtime. We implemented a uEyes prototype for supervising elderly people and performed some experiments based on several scenarios. Based on those experimental results, we confirmed that the social contexts are handled effectively to support the supervision.
This paper presents a gentle system for supervising care-support services that fulfill users' actual requirements based on their physical locations and statuses of system components in ubiquitous computing environments. To address issues of traditional supervisory systems based only on location information and the situation of one side of the watching site or the watched site, we consider all the detailed situations (contexts) of associated entities such as devices, software, networks, and users, in both sides, in addition to the users' physical locations. We propose a ubiquitous supervision system called uEyes to realize this. We introduce an autonomous decision making ability and cooperative behavior to each entity in uEyes, based on agentbased computing technologies. Using advanced features of the entities, live video streaming systems for watching over people can be constructed autonomously according to the multiple contexts of the entities, on both sides in runtime. We implemented a prototype system of uEyes for watching over elderly people, and performed some experiments based on several scenarios. For instance, we assumed a scenario in which a son, who is caring for his ailing father, needs to see his father's facial color and expression with high-quality video. For that purpose, a live video streaming system involving a high-resolution camera and a display device was autonomously configured in run-time. We confirmed that supervision services that fulfill detailed users' requirements can be provided effectively.
In this paper we propose a gentle system for watching over for ubiquitous care-support services that fulfill users' actual requirements. In traditional systems of this domain, only the location information of a user on "one" side of the watching site or the watched site is considered. In this work, we cope not only with the user's location but also all the detailed situations (contexts) of associated entities such as devices, software, networks, and users, in "both" sides. To do this, we propose a ubiquitous watching over system "uEyes". In uEyes, autonomous decision making ability and cooperative behavior are introduced to each entity. Based on the advanced feature of the entities, live video streaming system is dynamically constructed according to the contexts of the entities, on both sides in runtime. As a result, watching over service that fulfills detailed users' requirements can be effectively provided.We implemented a prototype of uEyes for watching over elderly people, and performed some experiments based on several scenarios. For instance, we assumed a scenario that a son, who is taking care of his ailing father, wanted to see his facial color and expression in high quality video. We confirmed that the live video streaming system involving high resolution camera and display devices is dynamically configured in run-time.
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