© 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


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！


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


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


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


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%!


Initial Approach

Term

Sequence

Bayesian Network

Access Log

Candidates

time

Second Stage

First StageAct

Detect a Behavior!!


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


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


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


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


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


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

© 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...