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 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 Hospital 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 Hospital 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
REF developed an approach for automatically estimating hospital-staff activities by training discrete HMMs for mapping contextual information to user activities.
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science