Download Unsupervised Transfer learning of activities in smart

Candidate: Parisa Rashidi
Advisor: Diane J. Cook
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Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer Learning
 Active learning
 Results
 Conclusions & future directions
2
Smart Homes
 Sensors & actuators integrated into everyday objects
 Knowledge acquisition about inhabitant
Percepts
(sensors)
Agent
Environment
Actions
(controllers)
3
Applications
 Energy efficiency
 Security
 Achieving more comfort
 Monitoring well-being of residents
 In home monitoring



4
Monitor daily activities
Check for anomalies
Help by giving prompts and cues
Activity Recognition
 A vital component of smart homes
 Recognizing activities from stream of sensor events
…
A
B
C
D
An Activity
(Sequence of sensor events)
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A
C
D
F
…
A Sensor Event
Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
6
Why it is difficult?
 Human activity is erratic and complex
 Discontinuous (interrupting events)
 Step order might vary each time
 Inter-subject and intra-subject variability
 The algorithm should be scalable
 Data annotation

Costly and laborious
 Training for each new space?
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Unsolved Challenges
 Many methods proposed
 Hidden Markov models, conditional
random fields, naïve Bayes, …
 Current methods
 Consider many simplifying assumptions
 Mostly are supervised

Data annotation problem
 Even if unsupervised
 Trained for each new setting from scratch
 Ignore activity variations or interruptions
 …
8
Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
9
Our Solutions
 Discovering complex activities
 Sequence mining
 Discovery activities from stream
 Stream sequence mining
 Transferring activity models to new spaces
 Transfer learning
 Guiding activity annotation
 Active learning
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Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
11
Sequence Mining
 Sequence
 Ordered set of items
 Examples
 Speech: sequence of phonemes
 DNA sequence: AAGCTACGTAA
 Network: sequence of packets
 Our data: sequence of sensor events
 Goal
 Finding repetitive sequential patterns in data
 Many methods proposed
 GSP, PrefixSpan, SPADE, …
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Activity Sequence Mining Problem
 Data: a single sequence with no boundaries
 Unlike transaction data
 We are looking for activity sequence patterns
 With discontinuous steps
 Variations of the same activity
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Transaction ID
Items
1
{Milk, Egg, Bread}
2
{Bread, Beer}
3
{Soap, Milk, Egg}
Item-set boundary
… M D M D A
C
D F
No boundaries !
…
From Sequence Mining to Activity
Recognition
 Find activity patterns
 Discontinuous Varied Sequence Mining (DVSM)
 Continuous, varied Order, Multi Threshold (COM)
 Cluster similar patterns
 Cluster centroid is a representative activity.
 Recognize activities
 Hidden Markov Model
Data
DMSM
Sensor
Data
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Clustering
Interesting
Patterns
Recognition
Representative
Activities
DVSM
Pattern Instances
 Finds general patterns/variations in
{b,x,a}
{a,b,q}
several iteration
 During each iteration

<a,b>
Finds increasing length patterns

Extend by prefix and suffix at each iteration
Checks if it is a variation of a general pattern
 At the end of each iteration
 Retain only interesting patterns according to
MDL principle

Compression
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Continuity
General Pattern
{a,u,b}
DVSM
 Continuity
 Pattern  Variations  Instances  Events
abchdadcbopa
bb
cgeqydc
arhabxc
 Prunes patterns/variations with low compression values
 Highly discontinuous
 Infrequent
 Prunes non-maximal patterns
 Prune irrelevant variations using mutual information and
sensor
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Improve DVSM: COM
 Different sensor frequencies for
 Different regions of home
 Different types of sensor
 “Rare item problem”
 A global min-support doesn’t work!
 Use multiple support thresholds
f k  0.02
f m  0.02
f k  0.02
f m  0.02
f k  NA
f m  0.03
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f k  0.01
f m  0.03
f k  NA
f m  0.06
Frequent Motion Sensors
Frequent Key Sensors
Infrequent Motion Sensors
Infrequent Key Sensors
Clustering
 Grouping similar objects together
 There are many different clustering methods
 Partition based (k-Means)
 Hierarchal (CURE)
 Density based (DBSCAN) Centroid Activity Activity Cluster
 Model based (EM)
.
.
. .. .
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...
.
. ... ..
.... .. .
Similarity Measure
 How similarity is determined?
 Our activity similarity measure
Total Similarity
=
Start Time Similarity
+
Duration Similarity
+
Structure Similarity
+
Location Similarity
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Activity Recognition
 Basically a sequence classification problem
 Different than ordinary classification problems
 Variable length records
 Order
 Probabilistic methods are the most widely used
HMM
 Markov chains
 Hidden Markov models
 Dynamic Bayesian Networks
 Conditional random fields
Day
X
DBN
Day
X
Time
Y
Room
Activity n
Time t+1
Time t
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Room
Y
Activity n
Time t
Time
Time t+1
Hidden Markov Model
 A statistical model
 Markovian property
 A number of observed & hidden variables

Their transition probabilities
 We automatically build HMM from cluster centroids
a12
Cooking
a21
Taking
Meds
b22
b11
b13
a23
a34
Hygiene
Leaving
b23
b35
b12
b46
b33
M003
21
D029
M001
b34
D032
b45
M006
M004
Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
22
Stream Mining
 Many emerging applications
 IP network traffic
 Scientific data
 Process data as it arrives
 We cannot store all data
 One pass
 Approximate and randomization answers
 E.g. relaxed support threshold
 Some proposed methods
 Frequent itemset mining

Lossy counting [Manku 2002], SpaceSaving algorithm [Metwally 2005], …
 Frequent sequence mining
 SPEED algorithm [Raissi 2005], ..
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Tilted Time Model
 Uses a set of time-tilted windows to keep frequency of
items
 Finer details for more recent time frame
 Coarser details for older time frames
 Shifting history into older time frames as data arrives
Month
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day
hour
*C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu, Mining Frequent Patterns in Data Streams at Multiple
Time Granularities. MIT Press, 2003, ch. 3.
Tilted Time Model
 Minimum support: σ
 Maximum support error: ε
 An itemset can be
 Frequent
 Sub-frequent
 Infrequent
 Pruning itemsets (tail pruning)
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StreamCOM
 Extending COM into a stream mining method
 Using tilted time model
COM
StreamCOM
Titled
Time
Model
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Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
31
Transfer Learning
 Apply skills learned in previous tasks to novel tasks
 Chess  Checkers
 Math  CS
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test items
training items
Transfer Learning
test items
training items
Traditional ML
Why in Smart Homes?
 Why transfer learning?
 Supervised methods

Requires annotation
 Unsupervised methods

Requires lots of data
Target Home
Infinite Stream of Dafa
Small Initial Dataset
Source Home
Activity
Pattern
Mapping
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Labeled
Activity
Patterns
Activity
Recognition
Our Transfer Learning Solutions
 Activity Transfer
 Transfer from one resident to another
 Different residents, space layouts, sensors


Transfer from a single physical source to a target
Transfer from multiple physical source to a target
 Domain selection
Transfer
Source
Activities
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Target
Activities
Multi Home Transfer Learning
(MHTL)
1.
Find activity models in both spaces
 Source: extract activity model
 Target: location based mining, incremental clustering
 Activity consolidation, sensor selection
2. Map activity models from source to target
 Map Sensors
 Map activities
3. Map Labels
4. Use labels for recognition!
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MHTL Architecture
Input
Activity
Extraction
Mapping
Recognition
Form
Activities
Initialize
Source
Labeled
Data
Consolidate
Activities
Activity
Templates
Map
Sensors
Select
Sensors
Target
Unlabeled
Data
Target
Labeled
Data
(If any)
Mine Data
Activity
Templates
Form
Activities
Consolidate
Activities
Select
Sensors
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Adjust
Mapping
Map
Activities
Target
Labeled
Activities
Domain Selection
 Our previous works
 Assumed “all sources are equal”
 Not all sources are equal
 Some sources are more equal!
 Select top N sources
 Efficiency: do not use all sources
 Accuracy: negative transfer effect
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Some animals are more equal ...
George Orwell – Animal Farm
Domain Similarity
 How to measure difference between two distributions?
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Domain Similarity
 Conventional similarity measures
 Kullbeck Leibler divergence (KL), Jensen Shannon
divergence (JSD), L1 or Lp norms
 Kifer et al [2004] proposed H distance
 Later Ben David et al [2007] proved that
 It is exactly the problem of minimizing the empirical
risk of a classifier that discriminates between instances
drawn from the two domain!
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Demonstration of H Distance
H-distance: 0.1, small!
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*Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for
domain adaptation. In NIPS, 2007.
Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
47
Active Learning
 The learning algorithm can query for the label of a
point
 Ask the oracle!
 Proposed methods
 Uncertainty sampling, committee based, …
Select
Informative
Instance
Learning
Algorithm
?
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Informative
Instance
Oracle
Label
A Problem!
 Traditional active learning methods
 Ask overly specific queries
vs.
“What is the class label if
(sex= female) and (age =39) and (chest
pain type =3) and (serum cholesterol =
150.2 mg/dL) and (fasting blood sugar =
150 mg/dL)... and (electrocardiographic
result = 1) and (maximum heart rate
achieved = 126) and (exercise induced
angina = 90) and (heart old peak = 2.3)
and (number of major vessels colored by
fluoroscopy = 3)? ”
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“What is the class label
if (age > 65) and (chest pain type = 3)
and (serum cholesterol > 240 mg/dL) ?”
Template Based Queries
 Select the most informative instances
 Select friends (+) and enemies (-) = Δ
 Select relevant and weakly relevant features in Δ
 Build a template query using relevant and weakly
relevant features
Data
Learning
Algorithm
Select
Informative
Instance
Build Template
Query based on
Template Neighbors and
Enemies
Query
Oracle
Update
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Label
Select
Neighbors
and Enemies
RIQY
 RIQY: Rule Induced active learning QuerY method
 Select the most informative instances
 Select friends (+) and enemies (-) = Δ
 Use rule induction to build generic queries
Data
Learning
Algorithm
Select
Informative
Instance
Oracle
Update
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Label
Rule
Select
Neighbors
and Enemies
Induce Rule
based on
Neighbors and
Enemies
Agenda
 Introduction
 Challenges
 Solutions
 Sequence mining
 Stream mining
 Transfer learning
 Active learning
 Results
 Conclusions & future directions
53
Can we discover activities?
 DVSM vs. COM
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Activity Discovery
 Confusion matrix for various activities in apartment 1
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Some Discovered Patterns
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StreamCOM
 Taking medication activity
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Transferring Activities
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Transferring Activities
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What about active learning?
Wisconsin breast cancer
dataset -UCI repository
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Kyoto smart apartment
dataset -CASAS
Conclusions
 Two novel sequence mining methods
 DVSM
 COM
 A novel stream data mining method
 StreamCOM
 A couple of transfer learning methods
 Between residents
 Between one/multiple smart homes
 Source selection
 Two novel active learning methods
 Template based active learning
 RIQY
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Future Work
• Anomaly detection in sequences
• Exploiting more temporal information
• Order of activities
• Change detection in patterns
• …
62
Publications
 Published/Accepted
 Parisa Rashidi and Diane J. Cook. Mining and Monitoring Patterns of Daily
Routines for Assisted Living in Real World Settings. Proceedings of
International Health Informatics Conference (IHI). 2010.
 Parisa Rashidi and Diane J. Cook. Transferring learned activities in smart
environments between different residents. Proceedings of International
Conference on Intelligent Environments (IE), volume 2, pages 185-192.
Springer-Verlag, 2009.
 Parisa Rashidi and Diane J. Cook. Multi Home Transfer Learning for
Resident Activity Discovery and Recognition. Proceedings of International
Workshop on Knowledge Discovery from Sensor Data (KDD), pages 53-63,
2010.
 Parisa Rashidi, Diane J. Cook, "Home to home transfer learning",
Proceedings of AAAI Plan, Activity, Intention Recognition Workshop
(AAAI), 2010.
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Publications
 Published/Accepted
 Parisa Rashidi, Diane J. Cook, "Transferring Learned Activities and Cues
between Different Residential Spaces", Journal of Pervasive and Mobile
Computing (PMC). March 2010.
 Maureen Schmitter-Edgecombe, Parisa Rashidi, Diane J. Cook, Larry
Holder. Discovering and Tracking Activities for Assisted Living, The
American Journal of Geriatric Psychiatry. In Press, 2010.
 Parisa Rashidi, Diane J. Cook, , Larry Holder, Maureen SchmitterEdgecombe. Discovering Activities to Recognize and Track in a Smart
Environment, IEEE Transaction of Data and Knowledge Engineering
(TKDE). In Press, 2010.
 Parisa Rashidi, Diane J. Cook, Mining Sensor Streams for Discovering
Human Activity Patterns Over Time. Proceedings of International
Conference on Data Mining (ICDM), 2010.
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Publications
 Submitted
 Parisa Rashidi, Diane J. Cook. Domain Selection and
Adaptation in Smart Homes. ICOST 2011, January 2011,
submitted.
 Parisa Rashidi, Diane J. Cook. Template Based Active
Learning. AAAI 2011, February 2011. Submitted.
 Parisa Rashidi, Diane J. Cook. Ask Me Better Questions.
Rule Induction Based Active Learning. KDD 2011,
February 2011. Submitted.
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Publications
 Invited/To be submitted
 Parisa Rashidi, Diane J. Cook. Mining and Monitoring
Patterns of Daily Routines for Assisted Living in Real
World Settings. ACM Transactions special issue on
Intelligent Systems for Health Informatics. Invited. April
2011
 Parisa Rashidi, Diane J. Cook. Generic Active Learning
Queries. TKDE or JMLR. May 2011. To be submitted.
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Questions?
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