© 2005 Ritsumeikan Univ. All Rights Reserved. Embedded Action Detector to Enhance Freedom from Care...
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Transcript of © 2005 Ritsumeikan Univ. All Rights Reserved. Embedded Action Detector to Enhance Freedom from Care...
© 2005 Ritsumeikan Univ. All Rights Reserved.
Embedded Action Detector to Enhance Freedom from Care
Ritsumeikan University
Graduate School of Computer Science
Data Engineering Laboratory
Kyohei Koyama
© 2005 Ritsumeikan Univ. All Rights Reserved.
Tagged World
Pocket Assistant
Access Log
Detect you going out
RFID Tag
LeavingWithout locking
Leaving something
behind
Coordination
Alert!
Leaving the stove on
Ubiquitous FacilityService!
© 2005 Ritsumeikan Univ. All Rights Reserved.
Main Subject of This Presentation
The Pocket Assistant is an embedded computer, thus it only has limited power resources
The load can be huge, because the Pocket Assistant inspects all logs for each every access to the objects
The new way to reduce the load, keeping the accuracy of human activity recognition
© 2005 Ritsumeikan Univ. All Rights Reserved.
Definitions of Human Activity
The human activity is composed of three elements
Act : A Minimum unit of human activity
i.e. an access to an object Action : A sequence of acts Behavior : A set of actions
© 2005 Ritsumeikan Univ. All Rights Reserved.
Definitions of Human Activity
Turning the knob
Undoing the door chain
Pushing the power button
Unlocking the door
Opening the door
Putting on shoesTurning off TV
Going out
Behavior
Action
Act
Having a bag
Having baggage
Taking the remote control
Puttingon shoes
Takinga shoehorn
Taking shoes
© 2005 Ritsumeikan Univ. All Rights Reserved.
Bayesian Network
Bayesian Network methodology is applied for inspecting the access logs
Shoes
Chain Result(Going outside)
ProbabilityPropagation
Observed Value
is Assigned
Probability Variable
is Changed
Look
Knob
Shoehorn Door Key The probability of user going outside is 78%!
© 2005 Ritsumeikan Univ. All Rights Reserved.
Initial Approach
Term
Sequence
Bayesian Network
Access Log
Candidates
time
Second Stage
First StageAct
Detect a Behavior!!
© 2005 Ritsumeikan Univ. All Rights Reserved.
Experiment
“Going outside” behavior
Two kinds of cases are prepared
True case : When the user go outside
False case : When it looks like the user is
going outside, but actually staying home
324 cases have been sampled in total
© 2005 Ritsumeikan Univ. All Rights Reserved.
Ideas from Experiment (Threshold Value)
30
40
50
60
70
80
90
100
-10000 1000
20003000
40005000
60007000
80009000
10000
time(msec)
prob
abil
ity(
%)
False CasesFalse Cases
True CasesTrue Cases
Threshold Value
Threshold Value
© 2005 Ritsumeikan Univ. All Rights Reserved.
Ideas from Experiment (Key Event)
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11
Probability
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8
Probability
30
40
50
60
70
80
90
100
0 1 2 3 4 5
Probability
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8
Probability
BN1
BN2
BN3
BN4
Shoes
Shoehorn
Shoes
Lock
Key
Shoes
Lock
Graph 1 Graph 2
Graph 3 Graph 4
© 2005 Ritsumeikan Univ. All Rights Reserved.
Ideas from Experiment (Key Event)
The occurrence probability does not change dramatically when accesses other than the key event occur
It is reasonable to calculate the probability only when the Key Event occurs
The Key Event is effective to reduce the number of calculation for the probability of the Bayesian Network
© 2005 Ritsumeikan Univ. All Rights Reserved.
Revised Approach
time
Access Log
Bayesian Network
TriggerTrigger
Layoff(0.5~ 1.0sec)
Layoff(0.5~ 1.0sec)
Detection of Key Event
Detection of Key Event
Term
Sequence
Initial Approach
© 2005 Ritsumeikan Univ. All Rights Reserved.
Evaluation
Case IDInitial Approach Revised Approach
Number of Times Number of Times (%)
True
TC1TC2TC3TC4TC5TC6
1147853665937
27211518164
(23.68)(26.92)(28.30)(27.27)(27.12)(10.81)
FalseFC1FC2FC3
186776
222
(11.11)(2.99)(2.63)
Total - 14.80%
The revised approach reduces the number of calculation by 14.8% compared with the initial approach