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

ABCDACDF

An 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

13

Transaction IDItems1{Milk, Egg, Bread}2{Bread, Beer}3{Soap, Milk, Egg}MDMDACDF

Item-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

14

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

16

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

17

Clustering

Grouping similar objects together

There are many different clustering methods

Partition based (k-Means)

Hierarchal (CURE)

Density based (DBSCAN)

Model based (EM)

18

Similarity Measure

How similarity is determined?

Our activity similarity measure

19

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

20

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

21

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

24

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

26

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

28

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

30

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

32

Traditional ML

Transfer Learning

training items

test items

training items

test items

Transfer Learning Methods

33

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

33

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

34

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

35

Multi Resident Transfer Learning

Find interesting target patterns using DVSM

Cluster discovered patterns

Map cluster centroids to source activities

36

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!

37

MHTL Architecture

38

EM Framework

Updating sensor mappings probabilities

Updating activity mapping probabilities

39

Label Assignment

40

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

41

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!

43

Demonstration of H Distance

44

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,

48

A Problem!

Traditional active learning methods

Ask overly specific queries

49

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

50

RIQY

51

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

52

Agenda

Introduction

Challenges

Solutions

Sequence mining

Stream mining

Transfer learning

Active learning

Results

Conclusions & future directions

53

Can we discover activities?

DVSM vs. COM

54

Activity Discovery

Confusion matrix for various activities in apartment 1

55

Some Discovered Patterns

56

StreamCOM

Taking medication activity

57

Transferring Activities

58

Transferring Activities

59

What about active learning?

60

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.

63

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.

65

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.

66

Questions?

67

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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.

.

.

.

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.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Centroid Activity

.

.

.

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