Toward Mobile Sensor Fusion Platform for Context-Aware Services

To recognize a context of a user, it is important to know a physical condition of the user and a status of his/her surrounding environment. For example, to maintain a healthcare condition of an elderly person, his/her care workers require monitoring not only his/her physiological conditions but also physical statuses of his/her surroundings, such as room temperatures. However, most approaches to the context recognition have been focusing on either environmental-centric recognition or user-centric recognition. In ubiquitous computing researches, most approaches have focused on environment-centric recognition mechanism; they specifically examine how to recognize user’s activities by sensors embedded in an environment (Garlan et al., 2002; Roman et al., 2001). The recognition includes location of the users. On the other hand, in research fields of wearable mobile computing, most approaches have focused on user-centric recognition mechanism; they specifically examine activity recognition by using sensors worn by the users, such as wristwatch-type accelerometers (Van Laerhoven et al., 2008; Clarkson et al., 2000). Hence, context-aware systems that use both wearable body sensors and environmental sensors have been few. In this study, we specifically address context-aware services by using both wearable body sensors and surrounding wireless sensor networks, and propose a concept of mobile sensor fusion platform. Mobile sensor fusion platform is a new concept of a mobile service platform coordinating mobile phones and wireless sensor devices, such as wearable body sensors and environmental sensor networks. It will provide the following facilities to recognize user’s contexts: 1) communicating with wearable body sensors and surrounding wireless sensor devices via mobile phone, 2) analyzing and fusing the sensed data by cooperating with sensor middleware, which manages sensed data on a remote server and 3) providing context-aware services for users by using mobile phones, such as remote monitoring of physiological status. In this chapter, we propose our vision of mobile sensor fusion platform. First, we describe services, requirements, and two types of the fusion architecture: “mobile sensing architecture” and “stable sensing architecture.” Then, we propose a prototype platform, called CONSORTS-S (Sashima et al., 2008), of the mobile sensing architecture. As an exemplar service of the platform, we show a mobile healthcare service being aware of user’s physical conditions such as heartbeat, posture, and movement based on analysis of physiological signals (e.g., electrocardiograph, thermometer, and 3-axis accelerometer). The service can be also aware of environmental status (e.g., temperature).