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Discovery of Activity Patterns using Topic Models

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(1)

Presentation by Roland Meyer

(2)

Introduction

• Detect routines based on body movement

• Complex due to large variations in activities

(3)

Contributions

• New method to recognize daily routines

• Reusing an established method from text processing

• Applicable without user annotation

(4)

Topic Models

• Used for text processing for classification

• Collection of words (“Bag-of-words”)

• Unsupervised

(5)

Topic Models

(6)

Daily Routine Modeling

(7)

Data collection

• 1 person

• 16 days

• 2 wearable sensors

• Accelerometer

• Realtime clock

• 4 hours of memory

(8)

Annotation

• Online annotation

• Periodic set of questions on cell phone

• Time diary

• Occasional snapshots

• Offline annotation

• User could correct / complement data

• Used as ground truth

(9)

Discovering activities

• 34 distinct activities

• Mean, variance, frequency from acceleration sensors

• Combined with time-of-day

• SVMs, HMMs, Naive Bayes evaluated as classifiers

• 72.7% accuracy

• Great variations

• Problems with short and similar tasks

(10)

Discovering topics

• Latent Dirichlet Allocation on activity data

• Sliding window of 30 min. over activity stream

• 10 topics

(11)
(12)

Results on Discovering topics

• Precision and recall calculated for 6 of 7 day to cross- validate results

• Supervised classifier using HMMs to calculate baseline

(13)

Unsupervised learning

• Get rid of user annotations

• Labels from data clustering

(14)

Future work

• Semi-supervision

• Noise modeling

• Include location information

• More users with more diverse lives

• Build applications

• Use better sensors (more memory)

(15)

Including location

• “Discovering Daily Routines from Google Latitude with Topic Models”

by Laura Ferrari and Marco Mamei

• “Discovering Human Routines from Cell Phone Data with Topic Models”

by Katayoun Farrahi and Daniel Gatica-Perez

(16)

Including location

“Discovering Daily Routines from Google Latitude with Topic Models” - Laura Ferrari and Marco Mamei

(17)

“Discovering Daily Routines from Google Latitude with Topic Models” - Laura Ferrari and Marco Mamei

(18)

Including location

“Discovering Human Routines from Cell Phone Data with Topic Models” - Katayoun Farrahi and Daniel Gatica-Perez

(19)

Reviews

• Average score: 1.75 (accept)

• Solid ground truth

• Privacy not addressed

• Spelling errors, graphs badly placed

• No automation, data needs to be manually copied

(20)

Referenzen

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