A Novel Sequence Representation for Unsupervised Analysis of Human Activities

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# A Novel Sequence Representation for Unsupervised Analysis of Human Activities - PowerPoint PPT Presentation

A Novel Sequence Representation for Unsupervised Analysis of Human Activities. Presented by: Wei Pan For CS88/188. The Unsupervised Activity Classification System. Length . A 40-page paper. Straight-forward way of thinking of a problem. No graph model, no inference, no fancy math.

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### A Novel Sequence Representation for Unsupervised Analysis of Human Activities

Presented by: Wei Pan

For CS88/188

Length
• A 40-page paper.
• Straight-forward way of thinking of a problem.
• No graph model, no inference, no fancy math.
Definition
• Key Object
• Fridge, washer, stove, sink…
• Event
• Interaction among a subset of key objects in a certain time. (turn stove on; eat egg; fry egg)
• Activity
• A sequence of events with temporal order.
• Activity Structure
• The event sequence of an activity.
Definition
• n-gram Histogram
• An Activity could be represented by a subset of its sequence.
Definition
• Is n-gram real work?
• Under certain assumption it works!
• Simulation with VMMC.
• VMMC: A sampling method (in this paper) to generate sequences of different classes with noise.
Unsupervised Classification
• Distance Measurement.
• Clustering Algorithm.
• Cluster Modeling.
Problem #1
• Distance between two activities?
• Y, Z are events in A and B respectively. K is normalization factor.
Problem #2
• Clustering Algorithm
• A max clique is a class.
• Dominant set algorithm. ([Pavan2003])
Problem #3
• Each activity is one of the two types:
• Regular
• Anomalous
• Each class has typical nodes.
• Calculated through [Kleinburg99]
Problem #4
• How to understand anomalous activities in a class?

1 month,9am-5pm, 5 days a week

• 61 events, 10 key objects
• 195 activities, 150 labelled
• 7 major classes detected. (Table 1)
Residential House Sensor Data
• 5 months
• 16 Strain gages
• 16 event
• every day is an activity
Residential House Sensor Data
• People seems to have different plans for different day.
• 5 classes mined out. (Table 2)
• Yes, with a proper n
• 90% percent accuracy
Anomalous Analysis Works
• Discover some anomalous activities
• Truck left with door open
• Someone cleaning up the floor

### Activity-Class Characterization

Presented by: Wei Pan

For CS88/188

Assume some activities in class c

• 1-2-3-4-5
• 3-1-2-3-7
• 3-4-1-2-3-6-4-5
• 5-1-2-3-9-1

It seems 1-2-3 is very common in this class.

Find a sequence of events s, so that s will have a certain prediction power in all activities of class c. Thus s will be a motif of class c.

• Prediction power is analytically described as a bit-gain.