Location tracking: technology, methodology and...
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Location tracking: technology, methodology and applications Marina L. Gavrilova SPARCS Laboratory Co-Director Associate Professor University of Calgary
Transcript of Location tracking: technology, methodology and...
Location tracking: technology,
methodology and applications
Marina L. Gavrilova
SPARCS Laboratory Co-Director
Associate Professor
University of Calgary
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
Talk outline
1. Calgary Health Region RTLS Competition
2. Medical personnel tracking project description
3. Methodologies
4. Outcomes
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
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
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
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
Voronoi Diagram Raster Method Potential Method
Example: topology-based data structures to store information
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
era
tio
n
Example: adaptive tree-based data structure
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
Marina L. Gavrilova
Example 3D data visualization using adaptive LOD
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
Example: Clustering and data filtering
Original dataset Crystal output (Th = 2.5)
Original dataset Crystal output (Th = 2.4)
Example: Path planning and risk avoidance
Clearance = 12
Clearance = 0
Clearance = 7
Clearance = 0
Clearance = 8
Clearance = 0
Path follows shipping lanes
wherever possible
Example: Path planning with constraints and multiple overlays
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
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
