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Outline 1. Introduction 2. Hierarchical activity model 3. Inference 4. Learning 5. Experimental results 6. Application: Opportunity Knocks 7. Conclusions
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1. Introduction
Track and predict a user’s location Infer a user’s mode and predict when and where Infer the locations of transportation destinations. Indicate he or she has made an error.
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2. Hierarchical activity model
Locations and transportation modes Trip segments Goals Novelty
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two time slices k − 1 and k
novelty mode
next goal andcurrent trip segment
GPS sensor measurement
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Locations and transportation modes xk =〈 lk , vk , ck〉 zk : GPS sensor measurements
generated by the person carrying a GPS sensor
mk : transportation mode BUS, FOOT, CAR, and BUILDING.
The domain of τk is the set of outgoing neighbors of the current edge;
θk is picked from the edges close to zk
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Trip segments A trip segment is defined by its start location, tk.start, end
location, tk.end, and the mode of transportation, tk.mode.
: trip switching
: a counter that measures the time steps until the next transportation mode is entered.
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Goals & Novelty A goal represents the current target location of the person.
gk : goal
: goal switching node
nk : indicating whether a user’s behavior is consistent with historical patterns.
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3. Inference Flat model
Rao–Blackwellized particle filter for estimation in the flat model
RBPF algorithm for the flat model Hierarchical model
RBPF algorithm for the hierarchical model Detecting novel behavior
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Rao–Blackwellized particle filter for estimation in the flat model
Sampling step Kalman filter step Importance weights
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histories
location of the person
car location
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RBPF algorithm for the flat model
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Sampling step
Kalman filter step
Importance weights
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RBPF algorithm for the hierarchical model
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Detecting novel behavior
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4. Learning Finding mode transfer locations
estimate the mode transition probabilities using the expectation maximization (EM) algorithm
Finding goals a person typically spends extended periods of time whether
indoors or outdoors. Estimating transition matrices
use EM to estimate the transition matrices
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5. Experimental results
Activity model learning Empirical comparison to other models Error detection
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Activity model learning
six most common transportation goals frequently used bus stops and parking lots, the common routes using different modes of
transportation
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Empirical comparison to other models
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Error detection
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6. Application: Opportunity Knocks The name of our system is derived from the desire to
provide our users with a source of computer generated opportunities
it plays a sound like a door knocking to get the user’s attention.
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Opportunity Knocks(1)
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Opportunity Knocks(2)
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Opportunity Knocks(3)
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Opportunity Knocks(4)
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Opportunity Knocks(5)
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Opportunity Knocks(6)
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7. Conclusions provide predictions of movements to distant goals, and
support a simple and effective strategy for detecting novel events that may indicate user errors
system limitation : it uses f ixed thresholds to extract goals and mode transfer locations.
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