RESEARCH

Writers: Thor S. Prentow, Antonio J. Ruiz-Ruiz, Henrik Blunck, Allan Stisen, Mikkel B. Kjærgaard

Journal: Pervasive and Mobile Computing, Volume 16 
Year: 2015

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Abstract: 

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 hospital's 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 people's 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.  

Writers: Mikkel Baun Kjærgaard, Mads Vering Krarup, Allan Stisen, Thor Siiger Prentow, Henrik Blunck, Kaj Grønbæk, Christian S. Jensen

Conference: International Conference on Indoor Positioning and Indoor Navigation
Year: 2013

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Abstract: 

The past decade has witnessed substantial research on methods for indoor Wi-Fi positioning. While much effort has gone into achieving high positioning accuracy and easing fingerprint collection, it is our contention that the general problem is not sufficiently well understood, thus preventing deployments and their usage by applications to become more widespread.

Based on our own and published experiences on indoor Wi-Fi positioning deployments, we hypothesize the following: Current indoor Wi- Fi positioning systems and their utilization in applications are hampered by the lack of understanding of the requirements present in the real-world deployments. In this paper, we report findings from qualitatively studying organisational requirements for indoor Wi-Fi positioning.

The studied cases and deployments cover both company and public-sector settings and the deployment and evaluation of several types of indoor Wi-Fi positioning systems over durations of up to several years. The findings suggest among others a need for supporting all case-specific user groups, providing software platform independence, low maintenance, allowing positioning of all user devices, regardless of platform and form factor. Furthermore, the findings also vary significantly across organizations, for instance in terms of need for coverage, which motivates the design of orthogonal solutions.

Writers: Thor S. Prentow, Henrik Blunck, Kaj Grønbæk, Mikkel Baun Kjærgaard

Conference: IEEE International Conference on Mobile Data Management
Year: 2014

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Abstract: 

Accurate information about how people commonly travel in a given large-scale building environment and which routes they take for given start and destination points is essential for applications such as indoor navigation, route prediction, and mobile work planning and logistics.

In this paper, we propose methods for detecting commonly used routes by robust aggregation, clustering, and merging of indoor position traces. The developed methods overcome three specific challenges for detecting commonly used routes in an indoor setting based on position data: i) a high ratio between path-density and positioning-accuracy, ii) a flat path hierarchy, and iii) providing cost-effective scalability.

Through an evaluation based on data collected by staff members at a hospital covering more than 10 hectare over three floors, we show that the proposed methods detect routes that are representative of the commonly used routes between locations. These methods are sufficiently efficient to provide common routes based on real-time data from thousands of devices simultaneously.

Furthermore, we show that the methods operate robustly even on basis of noisy and coarse-grained position estimates as provided by large-scale deployable indoor Wi-Fi positioning systems, and with no prior information on building layout.

Writers: Allan Stisen, Nervo Verdezoto, Henrik Blunck, Mikkel Baun Kjærgaard, and Kaj Grønbæk

Conference: Computer-Supported Cooperative Work & Social Computing (CSCW) 
Year: 2016

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Abstract: 

The cooperative, invisible non-clinical work of hospital orderlies is often overlooked. It consists foremost of transferring patients between hospital departments. As the overall efficiency of the hospital is highly dependent on the coordination of the work of orderlies, this study investigates the coordination changes in orderlies’ work practices in connection to the implementation of a workflow application at the hospital. By applying a mixed methods approach (both qualitative and quantitative studies), this paper calls for attention to the changes in orderlies’ coordination activities while moving from a manual and centralized form to a semi-automatic and decentralized approach after the introduction of the workflow application. We highlight a set of cross-boundary (spatial and organizational) information-sharing breakdowns and the challenges of orderlies in maintaining local and global coordination. We also present design recommendations for future design of coordination tools to support orderlies’ work practices.

Writers: Allan Stisen, Andreas Mathisen, Søren Krogh Sørensen, Henrik Blunck, Mikkel Baun Kjærgaard, Thor Siiger Prentow

Conference: International Conference on Pervasive Computing and Communications (PerCom)

 
Year: 2016

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Abstract:

Being aware of activities of co-workers is a basic and vital mechanism for efficient work in highly distributed work settings. Thus, automatic recognition of the task phases the mobile workers has many applications, e.g., efficient coordination of tasks by visualizing co-workers' task progress, automatic notifications based on context awareness, and record filing of task statuses and completions. This paper presents methods to sense and detect highly mobile workers' tasks phases in large building complexes. Large building complexes restrict the technologies available for sensing and recognizing the activities and task phases the workers currently perform as such technologies have to be easily deployable and maintainable at a large scale. The methods presented in this paper consist of features that utilize data from sensing systems which are common in large-scale indoor work environments, namely from a WiFi infrastructure providing coarse grained indoor positioning, from inertial sensors in the workers' mobile phones, and from a task management system yielding information about the scheduled tasks' start and end locations. We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on real-world data collected over 4 days of regular work life of the mobile workforce. The collected data yields 83 tasks in total involving 8 different orderlies from a major university hospital with a building area of 160,000 m2. The results show that the proposed methods can distinguish accurately between the four most common task phases present in the orderlies' work routines, achieving accuracies of 89.2%.

Writers: Thor Siiger Prentow, Henrik Blunck, Mikkel Baun Kjærgaard, Allan Stisen

Conference: International Conference on Mobile Computing, Applications and Services

 
Year: 2015

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Abstract:

Transportation mode detection (TMD) is a growing field of research, in which a variety of methods have been developed, foremost for outdoor travels. It has been employed in application areas such as public transportation and environmental footprint profiling. For indoor travels the problem of TMD has received comparatively little attention, even though diverse transportation modes, such as biking and electric vehicles, are used indoors. The potential applications are diverse, and include scheduling and progress tracking for mobile workers, and management of vehicular resources. However, for indoor TMD, the physical environment as well as the availability and reliability of sensing resources differ drastically from outdoor scenarios. Therefore, many of the methods developed for outdoor TMD cannot be easily and reliably applied indoors.

In this paper, we explore indoor transportation scenarios to arrive at a conceptual model of indoor transportation modes, and then compare challenges for outdoor and indoor TMD. In addition, we explore methods for TMD we deem suitable in indoor settings, and we perform an extensive real-world evaluation of such methods at a large hospital complex. The evaluation utilizes Wi-Fi and accelerometer data collected through smartphones carried by hospital workers throughout four days of work routines. The results show that the methods can distinguish between six common modes of transportation used by the hospital workers with a high accuracy.

Writers: Allan Stisen, Henrik Blunck, Mikkel Baun Kjærgaard, Kaj Grønbæk

Conference: Australian Computer-Human Interaction Conference on Designing Futures: the Future of Design

 
Year: 2014

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Abstract:

With the blooming of new available wrist-worn devices there are potentials for these to support the work done in many professional domains. One such domain is hospital service work. This paper explores two wearable prototypes' challenges and opportunities to support future hospital service work. This explorative study was conducted with experienced hospital orderlies who interacted with the management application across two wearable concepts, and one handheld smartphone in five scenarios in a hospital environment. This study shows that wearable computers can effectively support the maintenance work of the orderlies and has domain-specific advantages over the handheld smartphone, e.g., the former support glancing at the task information. 

Writers: Thor S Prentow, Henrik Blunck, Mikkel B Kjærgaard, Allan Stisen, Kaj Grønbæk

Conference: International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MOBIQUITOUS)

 
Year: 2014

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Abstract:

The ability to accurately estimate indoor travel times is crucial for enabling improvements within application areas such as indoor navigation, logistics for mobile workers, and facility management. In this paper, we study the challenges inherent in indoor travel time estimation, and we propose the InTraTime method for accurately estimating indoor travel times via mining of historical and real-time indoor position traces. The method learns during operation both travel routes, travel times and their respective likelihood---both for routes travelled as well as for sub-routes thereof. As input the method is designed to take generic position traces and is thus interoperable with a variety of indoor positioning systems. The method's advantages include a minimal-effort setup and self-improving operations due to unsupervised learning---as it is able to adapt implicitly to factors influencing indoor travel times such as elevators, rotating doors or changes in building layout. Our extensive evaluation uses datasets collected in real-world hospital work environments. InTraTime is deployed at a hospital as an online system, demonstrating that it learns automatically and in real-time travel times as position traces are collected within the building complex. Results indicate that InTraTime is superior with respect to metrics such as deployment cost, maintenance cost and estimation accuracy, yielding an average deviation from actual travel times of 11.7 %. This accuracy was achieved despite using a minimal-effort setup and a low-accuracy positioning system.