thesis research proposal supporting sensor fusion …whd/contextaware/presentationslides.pdf ·...
TRANSCRIPT
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Thesis Research ProposalSupporting Sensor Fusion for
Context-Aware Computing
Thesis Research ProposalThesis Research ProposalSupporting Sensor Fusion for
Context-Aware Computing
Huadong WuThe Robotics Institute
Carnegie Mellon University
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Sensing hardware: cameras, microphones, etc.Environment situation: people in the meeting room, objects around a moving car, etc.
humans understand
context naturally &effortlessly
Toward Context UnderstandingToward Context Understanding
Identification, representation, and understanding of contextAdapt behavior to context
traditionalsystem
ContextToolkitsystem
Toolkit+ SensorFusion
sensorsensor sensor
sensor
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Expectations of the Proposed SystemExpectations of the Proposed System
context AI rules
Widget
sensor
Widget
sensor
e.g. Dempster-Shafer Belief Combination
Sensor fusion mediator
Observations & Hypotheses
Dynamic ConfigurationTime interval: T
Sensor listUpdating flag
… …
Expected Performance BoostExpected Performance Boost
1. Uncertainty & ambiguityrepresentation to user applications
2. Information consolidation &conflict resolving for users
3. Adaptive sensor fusion support switch to suitable algorithms
4. Robust to configuration change — and for some to die gracefully
5. Situational description support— using more & complex context
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What is “Supporting Sensor Fusion” about?
What is “Supporting Sensor Fusion” about?
→ ⋅)( mapping info f
sensory data
ms
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context facts/events
SF( )SFSF( )
• Sensor Fusion:
• Supporting Sensor Fusion:
1. Define context status vector & sensory data vector
2. Seek appropriate information mapping function
3. Specify Software architecture and provide software modules/libraries to implement the mapping
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Network connected ubiquitous computing
What is “Context-Aware Computing” about?
What is “Context-Aware Computing” about?
-car: web-browser and
information center on wheelshouse: intelligent home automation
Smart office
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Context-Sensing: the Heart of Context-Aware Computing
Context-Sensing: the Heart of Context-Aware Computing
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context inform
ation tagging
presen
tation
execu
tion
•Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves.
•A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task
— Dey et al., “Towards a Better Understanding of Context and Context-awareness”, CHI 2000
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Context-Sensing Frustration & Opportunity Context-Sensing Frustration & Opportunity
sound processing: speaker recognition, speaking understanding
image processing: face recognition, object recognition, 3-D object measureing
location, altitude, speed, orientation
ambient environment
personal physical state: heart rate, respiration rate, blood pressure, blink rate, Galvanic Resistance, body temperature, sweat
microphones cameras, infra-red sensors
GPS, DGPS, serverIP, RFID, gyro, accelerometers, dead-reckoning
network resource, thermometer, barometer, humidity sensor, photo-diode sensors, accelerometers, gas sensor
biometric sensors: heart-rate/blood-pressure/GRS, temperature, respiration, etc., …
information: sensors
space time socialoutside nowactivity schedule
inside history
context
self, family, friends, colleague, acqauintance, etc.
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Context-Sensing Methodology (1): Sensory Data to Context MappingContext-Sensing Methodology (1): Sensory Data to Context Mapping
context
observations& hypotheses
sensory output
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moodagitation / tiredness
stress concentration preferencesphysical information
merry, sad, satisfy…
nervousness focus of attention
habits, current name, address, height, weight, fitness, metablism, etc.
inside (personal information, feeling & thinking, emotional)
Starting Point: Context Classification and Modeling
Starting Point: Context Classification and Modeling
work body visionaural (listen/talk)
hands
task ID drive, walk, sit, …
read, watch TV, sight-seeing, …, people: eye contact
content: work, entertainment, living chore, etc., …
type, write, use mouse, etc., …
interruptable…
activity
location proximity time people audiovisualcomputing & connectivity
city, altidude, weather (cloudyness, rain/snow, temperature, humidity, barometer pressure, forecast), location and orientation (absolute, relative to
close to: building (name, structure, facilities, etc. knowledge), room, car, devices (function, states, etc.), …, vicinity temperature, humidity, vibration,
day, date individuals or group (e.g. audience of a show, attendees in a cock-tail party): people interaction, casual chatting, formal meeting, eye contact, attention arousing; non
human talking (information collection), music, etc.; in-sight objects, surrounding scenery
computing environment (processing, memory, I/O, etc., hardware/software resource & cost), network connectivity, communication bandwidth, communication costchange:
travelling, speed, heading,
change: walking/running speed, heading
time of the day: office hour, lunch time, …, season of a year, etc.
interruption source: imcoming calls, encounting, etc., …
noise-level, brightness
history, schedule, expectation
social relationship
outside (environment)
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• Sensors are highly distributed • System configuration is often highly dynamic in mobile
environment• Economical limitations on sensor selection• Measurement resolution and accuracy requirements be
commensurate to human perception capabilities• Semantic description required
• Multiple sensor modalities and context informationsupport
Sensor Fusion Special RequirementsSensor Fusion Special Requirements
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Separate Context from Context-Sensing Implementation
Separate Context from Context-Sensing Implementation
Application Application Application
Context Information
Sensor Sensor Sensor
Context Component Architecture
Context Blackboard Architecture
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Georgia Tech Context-Aware Toolkit System Architecture
Georgia Tech Context-Aware Toolkit System Architecture
WidgetWidget
Service
Sensor Sensor
AggregatorInterpreter
Interpreter
DiscovererContext
Architecture
Application Application
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Object Hierarchy and Subclass Relationship in Context ToolkitObject Hierarchy and Subclass Relationship in Context Toolkit
BaseObject
Interpreter Discoverer
Aggregator
Widget
Service
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Georgia Tech Context Toolkit System:Georgia Tech Context Toolkit System:
1. Context specification2. Separation of concerns
and context handling3. Context interpretation4. Transparent distributed
communications5. Constant availability of
context acquisition6. Context storage7. Resource discovery
1. No intrinsic support for sensing uncertainty indication
2. No sensor fusion support
3. Unnecessary difficult to develop further when sensors’ pool is large
4. No common-knowledge or world-modeling context support
BenefitsBenefits LimitationsLimitations
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1. Complementary: incomplete data to more complete model
2.2. Competitive:Competitive:to reduce uncertaintiesto reduce uncertainties
3. Cooperative (cueing):data A depends on data B
Sensor Fusion TechnologySensor Fusion Technology
Context ToolkitContext Toolkit Needed PropertiesNeeded Properties
Who is there???
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Context Toolkit Extension’s SolutionContext Toolkit Extension’s Solution
WidgetWidgetService
Sensor Sensor
AggregatorInterpreter
Interpreter
DiscovererContext
Architecture
Application Application
Situation Abstraction
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user contextdatabase
appliance
embedded OS
Context-Sensing Methodology (2): Supporting with Context Aggregation
Context-Sensing Methodology (2): Supporting with Context Aggregation
database sever
gatewayInternet
Intranet
appliance
embedded OS
sensor
smart sensor node
sensor
smart sensor node
sensors
applicationssensor fusion
sensor
smart sensor node
appliance
embedded OS
site contextdatabase
context serverhigher-levelsensor fusion
applicationslower-level
sensor fusion
sensors
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Context-Sensing Methodology (3): Competitive Sensor Fusion SupportContext-Sensing Methodology (3):
Competitive Sensor Fusion Support
context
observations& hypotheses
sensory output
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Why Competitive Sensor Fusion? Why Dempster-Shafer Theory?
Why Competitive Sensor Fusion? Why Dempster-Shafer Theory?
#1. complementary
#2. competitive
#3. cooperativeParametric template,Figures of merit,Syntactic pattern recognition… …
Logical templateAI rule-based reasoning,Heuristic inferenceNeural network… …
Local minimal problem, results cannot be easily explained, not suitable for dynamic
configuration of sensors
It doesn’t make sense that a person is assigned as “0.6 membership of user A”, “0.7
membership of user B”, and “0.9 membership of either user A or B”
“cannot distinguish between lack of belief and disbelief”, cannot address a problem like “its
likely either user A or user B”
Though big improvement over Classic Inference method, still not powerful enough to
reason at fine granularity
Priori knowledge and pdf are required to combine multiple sensor outputs, priori
assessments are not used, do not have enough reasoning power
Flexible, powerful, no pdf needed, cheap computational cost in classification process
No pdf required, very cheap in computation
Likelihood of a hypothesis is updated using a previous likelihood estimation and additional
evidence
Associate pdf with confidence estimation, and provide a way to predict the result probabilities
of their boolean combinations
Sensor i: Pi( x detected | x appeared )Simple & effective for “x vs. ¬x” problems
Fuzzy Logic
Neural Network
Bayesian Network
Voting Fusion
Classic Inference
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Competitive Sensor Fusion with Dempster-Shafer Theory
Competitive Sensor Fusion with Dempster-Shafer Theory
•Frame of discernment : {{user A}, {user B}, {user A or B}, {neither user A or B}}
•
•
• Updated belief
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Competitive Sensor Fusion Architecture Support: Information Updating
Competitive Sensor Fusion Architecture Support: Information Updating
Widget
sensor
Widget
sensor
context AI rules
Dempster-Shafer Belief Combination
Sensor fusion mediator
Observations & Hypotheses
lower-levelsensor fusion
algorithm selection
Dynamic ConfigurationTime interval: T
Sensor listUpdating flag
… …
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DynamicContext
Database
System Architecture to Support Sensor Fusion
System Architecture to Support Sensor Fusion
1. Support sensor fusion with information mapping
2. Facilitate sensor fusion: applying AI more easily
3. Further separate context & context acquisition: sensors’ dynamic configuration
4. Easier to use complex context
user-mobile computer
site context database server
site context server
Widget
sensor
Widget
sensor
Widget
sensor
Widget
sensor
SF mediator
Aggregator
SF mediator
Aggregator
context data
ResourceRegistry
context data
ResourceRegistry
OtherAI algorithms
Interpreter
application application
OtherAI algorithms
Interpreter
AI algorithms
Performance Boost
Performance Boost
Discoverer
Dempster-Shafer rule
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Primary Concept-Demonstration System
Primary Concept-Demonstration System
camera
infrared camera
motion detector
microphone
fingerprint reader
microphone
IR/RF badge
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……
Preference-table-user[Hd]
Preference
144 lb ( = 4 lb)Weight
5’6” ( = 0.5” )Height
Huadong Wu ( =1.0)Name
Preference-table-user[Hd]
……
Preference-table-user[Hd]
Preference
144 lb ( = 4 lb)Weight
5’6” ( = 0.5” )Height
Huadong Wu ( =1.0)Name
Background-table-user[Hd]
……
Preference-table-user[Hd]
Preference
NSH 41029:06AM-10:55AM
Place Time
Huadong WuName
History-table-user[Hd]
Primary Demo System: Context Information Architecture
Primary Demo System: Context Information Architecture
6 ( > 0.5)Detected people #
User-tableDetected users
Current
Device-tableDevices
60 db ( = 6 db)Noise level
Brightness gradeLight condition
72 ºF ( = 3 ºF)Temperature
Area-tableArea
Room-table: NSH A417
User-tableDetected user
……
4 ( > 0.5)Detected people #
Device-tableDevices
60 db ( = 6 db)Noise level
Brightness gradeLight condition
72 ºF ( = 3 ºF)Temperature
NSH A417Of room
Inside Area
History-table-user[Chris]
10:45AM, 06/06/2001
Activity-table-user[Chris]
[0.4, 0.9]InsideBackground-table-user[Chris]
Chris
…
Background-table-user[Alan]
Background-table-user[Mel]
Background-table-user[Hd]
Background
…
History-table-user[Alan]
History-table-user[Mel]
History-table-user[Hd]
history
…
2:48PM, 06/06/2001
11:48AM, 06/06/2001
10:32AM, 06/06/2001
First detected
…
Activity-table-user[Alan]
Activity-table-user[Mel]
Activity-table-user[Hd]
Activity
…
Inside
Entrance
Entrance
Place
……
[0.9, 0.98]Alan
[0.3, 0.7]Mel
[0.5, 0.9]Hd
ConfidenceName
User-table
……
User-tableDetected user
2 ( > 0.5)Detected people #
Device-tableDevices
60 db ( = 6 db)Noise level
Brightness gradeLight condition
72 ºF ( = 3 ºF)Temperature
NSH A417Of room
Entrance Area
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Primary Demo System: from Sensor Observation to Context InformationPrimary Demo System: from Sensor Observation to Context Information
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2+22+1FR + IC
2+121Active Badge
2222Camera
22+21Infrared Camera (IC)
2+12+0Fingerprint Reader (FR)
1000Microphones
0012Motion Detector
Single UserA? B? C? …
Number of People
People There?
Moving Objects
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Primary Demo System: System Implementation
Primary Demo System: System Implementation
context database
site contextdatabase
sever
thermometer
smart sensornode
gateway
Internet
appliancesembedded OS
LAN
sensor fusionsignal processing
higher-levelsensor fusionweb server
database server
low-levelsensor fusion
systemmaintenance
mic
motiondetector
IR camera (future expansion)
camera
fingerprintreader
lightscontrol(future expansion)
applications
applications
wearablesensors
(wireless connection)
site contextsever
user mobilecomputer
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Secondary Concept-demo SystemSecondary Concept-demo System
Widget
sensor
Widget
sensor
context AI rules
Dempster-Shafer Belief Combination
Sensor fusion mediator
Observations & Hypotheses
lower-levelsensor fusion
algorithm selection
Time interval: TSensor list
Updating flag… …
Local Vehicle world Map (LVM)
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Secondary Demo System: Context and Jay Gowdy’s Sensor Fusion ArchitectureSecondary Demo System: Context and
Jay Gowdy’s Sensor Fusion Architecture
Hypothesis Pool
Hypothesis Generator
unused observations
new hypotheses
Sensor Fusion ProcessDEM GUI
Radar Lane Tracker Laser Striper
objects lanes objects, boundaries
Sensor Fusion ProcessSensor Fusion Process1. Sensors produce observations2. Observations fed to “Hypothesis Pool”3. Hypotheses strengthen with new data,
weaken in absence of data4. Unused observations generate new
hypotheses
Context InformationContext Information• Hypotheses from sensor fusion
(sensed objects)• Vehicle’s status (pose, speed, etc.) • Environment (downtown vs. highway,
road conditions, etc.)• Other, such as weather, time, driver’s
physiology status, etc.
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Secondary Demo System: Using “Additional” context informationSecondary Demo System: Using “Additional” context information
Environment conditions+ Vehicle speed constrains
To detect erroneous object-number assignment
To kick out outline points before doing Kalman filtering
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Working PlanWorking Plan� By May 15, 2001: building system computational architecture� Digest Georgia Tech’s Context-aware Toolkit, set up a context-aware system architecture
using the Context-aware Toolkit, make sure system components communicate properly� Set up database server and web server services, preliminary experiments on context
information repository mechanism
� By December 15, 2001: sensor network development� Choose sensors or do software simulation experiments on simple-case sensory row data
to context information mapping: function-enhanced widgets programming� Adopt or evaluate the IEEE 1451 smart sensing network standards on some sensors� Create or adjust context information repository mechanism using dynamic context
database services
� By May 15, 2002: sensor fusion study� Context taxonomy and knowledge presentation study, sensor fusion mediator programming � Implement context information structure model for given application scenario, stipulate and
program sensor-to-context mapping mechanism: test and improve widgets & the sensor fusion mediator
� Programming to implement Dempster-Shafer evidence combination mechanism for sensor fusion mediator
� By December 15, 2002: system refinement and evaluation� System architecture refinement and evaluation study, given the application scenario,
evaluate performance improvement with respect to sensor fusion and system scalability � System documentation, PhD thesis and defense
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• Define context architecture — context description model about the real world
• Mapping sensory data into the context model with system architecture support
Sensor Fusion for Context-Aware Computing: Summary
Sensor Fusion for Context-Aware Computing: Summary
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The Big Picture: How the Idea Works?The Big Picture: How the Idea Works?C
ompl
emen
tary
Sen
sor
Fus
ion
Coo
pera
tive
Sen
sor
Fus
ion
Com
peti
tive
Sen
sor
Fus
ion
Temporal dimensionP
hysi
cal d
imen
sion
fuse multiple measurements from the same sensor
--- at lower-level
fuse multiple sensors’ output (with Dempster-Shafer theory)
--- at higher-level
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• Demonstrate that under the guidance of well-defined context information architecture, synergistic interactions between sensor fusion and context information can greatly support and promote each other
• Propose and demonstrate the idea of supporting generalize-able sensor fusion processes in translating sensory data to semantic representations in an information structure
• Improve the system performance and adaptability of the quite well acclaimed Georgia Tech Context Toolkit systems
Expected ContributionsExpected Contributions