Unsupervised Transfer learning of activities in smart environments
SCALING ACTIVITY DISCOVERY AND RECOGNITIONTO LARGE, COMPLEX DATASETS
Candidate: Parisa Rashidi
Advisor: Diane J. Cook
1
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
3
Environment
Agent
Percepts (sensors)
Actions (controllers)
Applications
Energy efficiency
Security
Achieving more comfort
Monitoring well-being of residents
In home monitoring
Monitor daily activities
Check for anomalies
Help by giving prompts and cues
4
Activity Recognition
A vital component of smart homes
Recognizing activities from stream of sensor events
5
ABCDACDFAn Activity
(Sequence of sensor events)
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?
7
Unsolved Challenges
Many methods proposed
Hidden Markov models, conditional random fields, nave 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
10
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,
12
AGCTACCCGTTTA
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 IDItems1{Milk, Egg, Bread}2{Bread, Beer}3{Soap, Milk, Egg}MDMDACDFItem-set boundary
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
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DVSM
Finds general patterns/variations in several iteration
During each iteration
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
15
Pattern Instances
{b,x,a}
{a,b,q}
{a,u,b}
General Pattern
Continuity
Compression
DVSM
Continuity
Pattern Variations Instances Events
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 doesnt work!
Use multiple support thresholds
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Clustering
Grouping similar objects together
There are many different clustering methods
Partition based (k-Means)
Hierarchal (CURE)
Density based (DBSCAN)
Model based (EM)
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Similarity Measure
How similarity is determined?
Our activity similarity measure
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Total Similarity
Start Time Similarity
Duration Similarity
Structure Similarity
Location Similarity
=
+
+
+
Activity Recognition
Basically a sequence classification problem
Different than ordinary classification problems
Variable length records
Order
Probabilistic methods are the most widely used
Markov chains
Hidden Markov models
Dynamic Bayesian Networks
Conditional random fields
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Activity n
Time
Day
Room
Activity n
Time
Day
Room
Time t
Time t+1
X
Y
X
Y
Time t
Time t+1
HMM
DBN
Hidden Markov Model
A statistical model
Markovian property
A number of observed & hidden variables
Their transition probabilities
We automatically build HMM from cluster centroids
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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], ..
23
0100101111101111
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
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Month
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:
Maximum support error:
An itemset can be
Frequent
Sub-frequent
Infrequent
Pruning itemsets (tail pruning)
25
StreamCOM
Extending COM into a stream mining method
Using tilted time model
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COM
Titled Time Model
StreamCOM
Finds general patterns/variations in several iteration
During each iteration
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
27
Discovering Patterns
General Pattern
Variation
Variation
Variation
{b,x,c,a}
{a,b,q}
{a,u,b}
General Pattern
T(a) g Interesting
(g s) T(a) < g Sub- interesting
Otherwise uninteresting
Variation i
T(ai) Interesting
( v) T(ai) < Sub- interesting
Otherwise uninteresting
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Interesting Patterns
Average compression of all variations
Tail pruning
29
Pruning Patterns
General Pattern
Variation
Tail Pruning
To reduce the number of frequency records in the tilted-time windows
Prune old frequency records of an itemset
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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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Traditional ML
Transfer Learning
training items
test items
training items
test items
Transfer Learning Methods
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Yes
No
Yes
No
Yes
No
* S. Pan; Q.Yang; , "A Survey on Transfer Learning, IEEE TKDE, vol.22, no.10, pp.1345-1359, Oct. 2010
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Transfer Learning
Labeled Target Data?
Non-Inductive Transfer Learning
Labeled Source Data?
Unsupervised Transfer Learning
Transductive Transfer Learning
Same domains?
Inductive Transfer Learning
Labeled Source Data?
Self Taught Learning
Multi-Task Learning
Sample Selection/Covariance Shift
Domain Adaptation
Why in Smart Homes?
Why transfer learning?
Supervised methods
Requires annotation
Unsupervised methods
Requires lots of data
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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
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Multi Resident Transfer Learning
Find interesting target patterns using DVSM
Cluster discovered patterns
Map cluster centroids to source activities
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Multi Home Transfer Learning (MHTL)
Find activity models in both spaces
Source: extract activity model
Target: location based mining, incremental clustering
Activity consolidation, sensor selection
Map activity models from source to target
Map Sensors
Map activities
Map Labels
Use labels for recognition!
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MHTL Architecture
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EM Framework
Updating sensor mappings probabilities
Updating activity mapping probabilities
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Label Assignment
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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?
42
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
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H-distance: 0.1, small!
*Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for domain adaptation. In NIPS, 2007.
Domain Similarity
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!
45
Our Domain Selection Method
Find similarity of domains activity-wise
Overall similarity: average activity-wise similarity
Select n top sources
46
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,
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A Problem!
Traditional active learning methods
Ask overly specific queries
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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)?
vs.
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 (-) =
Select relevant and weakly relevant features in
Build a template query using relevant and weakly relevant features
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RIQY
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RIQY: Rule Induced active learning QuerY method
Select the most informative instances
Select friends (+) and enemies (-) =
Use rule induction to build generic queries
Details
The most informative instance
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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
58
Transferring Activities
59
What about active learning?
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Wisconsin breast cancer dataset -UCI repository
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
61
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 Schmitter-Edgecombe. 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.
64
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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Clustering
DMSM
Interesting
Patterns
Recognition
Representative
Activities
Data
Sensor
Data
Clustering
DMSM
Interesting Patterns
Recognition
RepresentativeActivities
a b ch dad c bo p b cg e q y d ar h abx ca bg e q y d c
Frequent Motion SensorsFrequent Key Sensors
0.02kf0.02mf0.02kf0.02mf0.01kf0.03mfkfNA0.03mfkfNA0.06mf
Infrequent Motion SensorsInfrequent Key Sensors
Activity Cluster
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Centroid Activity
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Cooking
Taking
Meds
Leaving
Hygiene
D029M003
M004M006
D032
M001
12a21a23a34a11b12b13b22b23b33b34b35b45b46b
()
T
a
b
%
()
T
a
b
%
Activity
Recognition
Labeled
Activity
Patterns
Small Initial Dataset
Infinite Stream of Dafa
Activity
Pattern
Mapping
Target Home
Source Home
text
Activity Recognition
Labeled Activity Patterns
Small Initial Dataset
Infinite Stream of Dafa
Activity Pattern Mapping
Target Home
Source Home
Source
Activities
Target
Activities
Transfer
Activity
Recognition
Labeled
Activity
Patterns
Small Initial Dataset
Infinite Stream of Dafa
Activity
Pattern
Mapping
Target
Resident
Source
Resident
text
text
Activity Recognition
Labeled Activity Patterns
Small Initial Dataset
Infinite Stream of Dafa
Activity Pattern Mapping
Target Resident
Source Resident
Mapping
Activity
Extraction
Recognition
Mine Data
Consolidate
Activities
Form
Activities
Consolidate
Activities
Form
Activities
Map
Activities
Map
Sensors
Adjust
Mapping
Initialize
Source
Labeled
Data
Target
Unlabeled
Data
Target
Labeled
Activities
Input
Target
Labeled
Data
(If any)
Select
Sensors
Select
Sensors
Activity
Templates
Activity
Templates
Learning
Algorithm
?
Select
Informative
Instance
Informative
Instance
Label
Oracle
text
text
Learning Algorithm
?
Select InformativeInstance
Informative Instance
Label
Oracle
Learning
Algorithm
Select
Informative
Instance
Select
Neighbors
and Enemies
Build Template
Query based on
Neighbors and
Enemies
Oracle
Update
Data
Label
Template
Query
text
text
Learning Algorithm
Select Informative Instance
Select Neighbors and Enemies
Build Template Query based on Neighbors and Enemies
Oracle
Update
Data
Label
Template Query
Learning
Algorithm
Select
Informative
Instance
Select
Neighbors
and Enemies
Induce Rule
based on
Neighbors and
Enemies
Oracle
Update
Data
LabelRule
text
text
Learning Algorithm
Select Informative Instance
Select Neighbors and Enemies
Induce Rule based on Neighbors and Enemies
Oracle
Update
Data
Label
Rule
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