A lthough researchers have developed robust approaches for estimating, location, and user identity, estimating user activities has proven much more challenging. Human activities are so complex and dynamic that it's often unclear what information is even relevant for modeling activities. Robust approaches to recognize user activities will require identifying the relevant information to be sensed and the appropriate sensing technologies.In our effort to develop an approach for automatically estimating hospital-staff activities, we trained a discrete hidden Markov model (HMM) to map contextual information to a user activity. (For other approaches to estimating work activities, see the related sidebar.) We trained the model and evaluated it using data captured from almost 200 hours of detailed observation and documentation of hospital workers. Here, we discuss our approach, the results, and how activity recognition could empower our vision of the hospital as a smart environment. The iHospitalThe iHospital is our vision of a highly interactive smart environment saturated with heterogeneous computing devices. These devices range from handheld computers that can capture and access limited amounts of information, to PCs that can be used at fixed sites for longer time periods, to conveniently located semipublic displays that let users share and discuss information. The iHospital staff can interact with the environment using a set of specialized services that account for contextual information. To help realize this vision, we have developed several ubiquitous and context-aware applications such as a context-aware mobile communication system, a context-aware map, and an activity-based application that support the following functionalities. 1,2 Awareness of people and artifactsHospital staff must navigate hospital premises to gather the information they need to conduct their work; this navigation is time-consuming. We developed and incorporated into the iHospital a contextaware application aimed at saving the staff time by making them aware of the presence, location, and status of artifacts and people, either using a digital floor map or an automatically updated list of people and artifacts. To introduce location awareness, we use a component that estimates users' positions throughout the hospital. 3 Context-aware communicationHospital staff can use contextual information as a trigger to retrieve or send messages. For example, a nurse can send a message to the physician responsible for a patient in the next shift when the patient's laboratory results are ready. The sender doesn't need to know which physician will be attending the patient or when the laboratory results will be ready. The application, on the basis of contextual information, automatically sends the message.In addition, hospital staff can transfer information from public spaces to personal devices, share

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Collaborative healthcare environments offer potential benefits, including enhancing the healthcare quality delivered to patients and reducing costs. As a direct consequence, sharing of electronic health records (EHRs) among healthcare providers has experienced a noteworthy growth in the last years, since it enables physicians to remotely monitor patients' health and enables individuals to manage their own health data more easily. However, these scenarios face significant challenges regarding security and privacy of the extremely sensitive information contained in EHRs. Thus, a flexible, efficient, and standards-based solution is indispensable to guarantee selective identity information disclosure and preserve patient's privacy. We propose a privacy-aware profile management approach that empowers the patient role, enabling him to bring together various healthcare providers as well as user-generated claims into an unique credential. User profiles are represented through an adaptive Merkle Tree, for which we formalize the underlying mathematical model. Furthermore, performance of the proposed solution is empirically validated through simulation experiments.