Location tracking: technology, methodology and applications

Transcription

1 Location tracking: technology, methodology and applications Marina L. Gavrilova SPARCS Laboratory Co-Director Associate Professor University of Calgary

2 Interests and affiliations SPARCS Lab Co-Founder and Director BT Lab Co-Founder and Director Computational Geometry and Applications Founder and Chair since 2001 ICCSA Conference series Scientific Chair (since 2003) Transactions on Computational Science Journal Springer Editor-in-Chief Research topics: optimization, reliability, geometric algorithms, data structures representation and visualization, GIS, spatial analysis, biometric modeling

3 Talk outline 1. Calgary Health Region RTLS Competition 2. Medical personnel tracking project description 3. Methodologies 4. Outcomes

4 CHR RTLS Competition Set-up CHR RTLS Call 7 large vendors responded Combination of software and hardware provider Evaluated by a committee for 3 months Decision made to approach a selected vendor for a limited trial Parameters Hardware, medical equipment, personnel Laptops, PDAs, and wireless devices Room-level accuracy Soft or hard thresholds Transmission coverage Security alerts

5 CHR RTLS Competition Criteria General Overall Vision for RTLS WiFi / RTLS experience Case Studies Provided Trial possibility Software Hardware Software Integration tools Security measures Adjustable for local settings Support/upgrades AutoCad, devices Physical characteristics (size, weight) Adjustable Radio Frequency Battery replacement Functionality

6 Research methodology: four phases Four phases of research: 1. Wi-Fi RFID Technology purchase and integration (initial trial phase: 20 units) at W21 site. 2. Initial data collection (over 15-day period) and validation through independent observers. 3. Tracking of temporal-spatial data related to nurses and MDs using RFID technology: location in time of doctors and nurses, contact with patients, use of medical devices, use of computers, use of hand-washing facilities, etc. 4. Location tracking of specific procedures where time involved in patient care can be more efficiently utilized, such as hand-washing behavior

7 Research methodology: proposed approaches Innovative approaches to data analysis and visualization developed in SPARCS Lab: Use of sophisticated topology-based methods for data representation and analysis (such as clustering, path planning, risk analysis, dependencies trends) Use of hierarchical weighted tree-based data structure with varied LOD (level of detail) for fast search and dynamic data updates Utilization of recently developed spatial analysis tools (autocorrelation, regression) for analysis of spatio-temporal trends and patterns Utilization of adaptive methods for data visualization (to improve space and time efficiency); Use of advanced interface design methods for improved visual reports and easy decision-making

8 Example: topology-based data structures to store information Voronoi Diagram Raster Method Potential Method

9 Interpolation Engine Cleanup Operation Error Metric Refine G Q Example: adaptive tree-based data structure DE M Wavelet Error Analysis Triangle Quad Tree Data structure Render

10 Example: converting Height field data into 3D topological mesh Pixel value (z) is used as Height Map Vertices are generated as points in 3D A Mesh is triangulated

11 Example 3D data visualization using adaptive LOD Marina L. Gavrilova

12 Example: Risk Analysis using Spatial Neighborhood Properties and Clustering Methods INCIDENTS DELAUNAY TRIANGULATION CLUSTERS SHIP ROUTE INTERSECTIONS CLUSTERING OF HIGH-RISK AREAS REDUCED VISIBILITY-GRAPH MINIMIZING RISK AT SEA Priyadarshi Bhattacharya and Marina Gavrilova, SPARCS Lab, Department of Computer Science, University of Calgary {pbhattac,

13 Example: Clustering and data filtering Original dataset Crystal output (Th = 2.5) Original dataset Crystal output (Th = 2.4)

14 Example: Path planning and risk avoidance Clearance = 12 Clearance = 7 Clearance = 8 Clearance = 0 Clearance = 0 Clearance = 0

15 Example: Path planning with constraints and multiple overlays Path follows shipping lanes wherever possible

16 Example: Spatio-temporal data analysis and visualization Average Tonnage of Tracks in each Grid Cell Average Tonnage of Incidents Average track counts Accident point counts

17 Research methodology: expected outcomes Expected outcomes: Knowledge outcomes, where research will produce new knowledge that is relevant to decision-making and policy-setting in health care; Improved patient-centred outcomes, particularly as a result of research that relates to the patient experience; Enhancement of processes in the complex clinical environment, which in turn will produce improvements in outcomes such as provider well-being, patient satisfaction, and improved patient-care policies; Cost and time saving outcomes Efficient resource utilization outcomes.

18 Questions? . Web