Allan Stisen

Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning

The optimization of logistics in large building complexes with many resources, such as hospitals, require realistic facility management and planning. Current planning practices rely foremost on manual observations or coarse unverified assumptions and therefore do not properly scale or provide realistic data to inform facility planning. In this paper, we propose analysis methods to extract knowledge from large sets of network collected WiFi traces to better inform facility management and planning in large building complexes. The analysis methods, which build on a rich set of temporal and spatial features, include methods for noise removal, e.g., labeling of beyond building-perimeter devices, and methods for quantification of area densities and flows, e.g., building enter and exit events, and for classifying the behavior of people, e.g., into user roles such as visitor, hospitalized or employee. Spatio-temporal visualization tools built on top of these methods enable planners to inspect and explore extracted information to inform facility-planning activities. To evaluate the methods, we present results for a large hospital complex covering more than 10 hectares. The evaluation is based on WiFi traces collected in the hospitals WiFi infrastructure over two weeks observing around 18000 different devices recording more than a billion individual WiFi measurements. For the presented analysis methods we present quantitative performance results, e.g., demonstrating over 95% accuracy for correct noise removal of beyond building perimeter devices. We furthermore present detailed statistics from our analysis regarding peoples presence, movement and roles, and example types of visualizations that both highlight their potential as inspection tools for planners and provide interesting insights into the test-bed hospital.

Robust Human Activity Recognition using smartwatches and smartphones

Abstract Smart user devices are becoming increasingly ubiquitous and useful for detecting the user’s context and his/her current activity. This work analyzes and proposes several techniques to improve the robustness of a Human Activity Recognition (HAR) system that uses accelerometer signals from different smartwatches and smartphones. This analysis reveals some of the challenges associated with both device heterogeneity and the different use of smartwatches compared to smartphones. When using smartwatches to recognize whole body activities, the arm movements introduce additional variability giving rise to a significant degradation in HAR. In this analysis, we describe and evaluate several techniques which successfully address these challenges when using smartwatches and when training and testing with different devices and/or users.

Clinical and Non-Clinical Handovers: Designing for Critical Moments

Recent studies call for attention to the relevant work that non-clinical personnel do, not only to support clinical work, but also to the overall healthcare information infrastructure. Based on a case study of orderlies work in a major university hospital, we further investigate and characterize orderlies work with particular focus on handover situations as these support the collaboration and continuous coverage of hospital work practices. The case study involves interviews with orderlies and clinicians and a pilot study of a task management application. Based on our findings, we describe three different types of handovers (clinical to non-clinical, non-clinical to clinical, and intra non-clinical), and highlight the challenges that arise during the main tasks with handovers situations: patient transport and patient mobilization. We discuss the importance of establishing and sustaining a common ground, supporting the alignment of tasks and negotiations, as well as enhancing the patient contact within and among clinical and non-clinical personnel. We also present a couple of design recommendations to account for these challenges and handover types in the design of handover tools to support orderlies work practices.