location tracking: technology, methodology and...
TRANSCRIPT
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Location tracking: technology,
methodology and applications
Marina L. Gavrilova
SPARCS Laboratory Co-Director
Associate Professor
University of Calgary
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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
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Talk outline
1. Calgary Health Region RTLS Competition
2. Medical personnel tracking project description
3. Methodologies
4. Outcomes
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CHR RTLS Competition
Hardware, medical equipment, personnel
Laptops, PDAs, and wireless devices
Room-level accuracy
Soft or hard thresholds
Transmission coverage
Security alerts
Parameters 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
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CHR RTLS Competition Criteria
Overall Vision for RTLS
WiFi / RTLS experience
Case Studies Provided
Trial possibility
Physical characteristics (size, weight)
Adjustable Radio Frequency
Battery replacement
Functionality
Software Integration tools
Security measures
Adjustable for local settings
Support/upgrades
AutoCad, 802.11 devices
General
Hardware
Software
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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
Research methodology: four phases
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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
Research methodology: proposed approaches
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Voronoi Diagram Raster Method Potential Method
Example: topology-based data structures to store information
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DE
M Inte
rpo
lati
on
E
ng
ine
Triangle Quad Tree
Data structure Err
or
Met
ric
Ref
ine
Render G Q
Wavelet
Error Analysis
Cle
an
up
Op
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tio
n
Example: adaptive tree-based data structure
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Example: converting Height field data into 3D topological mesh
200 255 150 100
100 255 255 200
200 150 200 100
• Pixel value (z) is used as Height Map
• Vertices are generated as points in 3D
• A Mesh is triangulated
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Marina L. Gavrilova
Example 3D data visualization using adaptive LOD
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INCIDENTS
SHIP ROUTE INTERSECTIONS CLUSTERING OF HIGH-RISK AREAS
DELAUNAY TRIANGULATION CLUSTERS
MINIMIZING RISK AT SEA
Example: Risk Analysis using Spatial Neighborhood
Properties and Clustering Methods
Priyadarshi Bhattacharya and Marina Gavrilova,
SPARCS Lab, Department of Computer Science,
University of Calgary
e-mail: {pbhattac, marina}@cpsc.ucalgary.ca
REDUCED VISIBILITY-GRAPH
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Example: Clustering and data filtering
Original dataset Crystal output (Th = 2.5)
Original dataset Crystal output (Th = 2.4)
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Example: Path planning and risk avoidance
Clearance = 12
Clearance = 0
Clearance = 7
Clearance = 0
Clearance = 8
Clearance = 0
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Path follows shipping lanes
wherever possible
Example: Path planning with constraints and multiple overlays
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Average Tonnage of
Tracks in each Grid
Cell
Average Tonnage of
Incidents
Average track counts
Accident point counts
Example: Spatio-temporal data analysis and visualization
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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.
Research methodology: expected outcomes