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Dieter Fox

Dieter Fox. University of Washington Department of Computer Science & Engineering.  Joint work with Students: Brian Ferris, Julie Letchner, Lin Liao, Don Patterson, Alvin Raj, Amarnag Subramanya, Danny Wyatt UW faculty: Jeff Bilmes, Gaetano Borriello, Henry Kautz

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Dieter Fox

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  1. Dieter Fox University of Washington Department of Computer Science & Engineering

  2.  Joint work with Students: Brian Ferris, Julie Letchner, Lin Liao, Don Patterson, Alvin Raj, Amarnag Subramanya, Danny Wyatt UW faculty: Jeff Bilmes, Gaetano Borriello, Henry Kautz Intel Research Seattle: Tanzeem Choudhury, Matthai Philipose  Funded by NSF, DARPA, MSR, Toyota 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 2

  3.  U.S. by 2050: 80 million people over age 65 and 13-16 million Alzheimer's patients  Goal: Develop technology to Support independent living by people with cognitive disabilities ▪ At home ▪ At work ▪ Throughout community Improve health care ▪ Long term monitoring of activities of daily living (ADL’s) ▪ Intervention before a health crisis 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 3

  4.  Transportation routines via Dynamic Bayes Nets  Significant places via Conditional Random Fields  Activity recognition projects  Discussion 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 4

  5. [Liao-Fox-Kautz: AAAI-04, AIJ-07]  Given data stream from a wearable GPS unit Infer the user’s location and mode of transportation (foot, car, bus, bike, ...) Predict where user will go Detect novel behavior / user errors 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 5

  6.  Dead and semi-dead zones near buildings, trees, etc.  Sparse measurements inside vehicles, especially bus  Multi-path propagation  Inaccurate street map  … 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 6

  7.  Map is directed graph  Location: Edge e Distance d from start of edge  Prediction: Move along edges according to velocity model  Correction: Update estimate based on GPS reading 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 7

  8. xk-1 xk Edge, velocity, position zk-1 zk GPS reading Time k Time k-1 Task: Estimate posterior over hidden states 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 8

  9. e1 x k-1 e3 ? e2 e0 ? Problem: Predicted location is multi-modal 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 10

  10. e1 x k zk e3 e2 Problem: GPS reading is not on the graph 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 11

  11. x k e1 x if =e1 q k zk e3 e2 Problem: GPS reading is not on the graph 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 12

  12. e1 x k zk x k e3 if =e2 q e2 Problem: GPS reading is not on the graph 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 13

  13. ek-1 ek Edge transition xk-1 xk Edge, velocity, position GPS association θ k θ k-1 zk-1 zk GPS reading Time k Time k-1 Task: Estimate posterior over all hidden states 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 14

  14. STREET MAP Source: Tiger / Line data BUS ROUTES / STOPS Source: Metro GIS RESTAURANTS / STORES Source: MS MapPoint 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 15

  15.  Represents posterior by sets of weighted particles: , { s S k   ( ) ( ) i i , ,..., 1  } w i n   Basic idea: Sample some variables of state space and solve other variables analytically conditioned on samples  Here: Each particle contains Kalman filter for location: , ,  v e s    ( ) ( ) ( ) ( ) ( ) 2 i i i i i , ( , ) N KF for position Edge transitions, velocities, edge associations  Update sets by sampling procedure (SISR) 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 16

  16. GPS measurements Particles (Kalmanfilters) 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 17

  17. mk-1 mk Transportation mode xk-1 xk Edge, velocity, position zk-1 zk GPS reading Time k Time k-1  ( ,   ( ) i ( ) i ( ) i ( ) i ( ) i ( ) i 2 , , , , ) s m e v N  Particles: 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 18

  18.  Encode prior knowledge into the model Modes have different velocity distributions Buses run on bus routes Get on/off the bus near bus stops Switch to car near car location 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 19

  19. Measurements Projections Bus mode Car mode Foot mode Green Red Blue 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 20

  20. A B Workplace  Goal (destination): workplace (home, friends, restaurant, ...)  Trip segments: <start, end, transportation> Home to Bus stop A on Foot Bus stop Ato Bus stop B on Bus Bus stop B to workplace on Foot 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 21

  21. [Liao-Fox-Kautz: AAAI-04, AIJ-07] gk-1 gk Goal tk-1 tk Trip segment mk-1 mk Transportation mode xk-1 xk Edge, velocity, position zk-1 zk GPS reading Time k Time k-1 [Bui-Venkatesh-West: JAIR-02] ( ) i  ( ,   ( ) i ( ) i ( ) i ( ) i ( ) ( ) i 2 i , , , , , , ) s g t m e v N  Particles: 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 22

  22.  Customized model for each user  Key to goal / path prediction and error detection  Use low level model to learn variable domains Goals: locations where user stays for long time Transition points: locations with high mode transition probability Trip segments: connecting transition points or goals  Use full model to learn parameters Transition probabilities (goals, trips, edges) via EM 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 23

  23. Predicted goal Predicted path 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 24

  24. GOING TO THE WORKPLACE GOING HOME High probability transitions: bus car foot 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 25

  25. 0.5 0.5 5 43 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 26

  26. [Patterson-Liao-etAl: Ubicomp-04] bk-1 bk Behavior mode normal / unknown / error gk-1 gk Goal tk-1 tk Trip segment mk-1 mk Transportation mode xk-1 xk Edge, velocity, position zk-1 zk GPS reading Time k Time k-1 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 27

  27. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 28

  28. Untrained flat model Trained model Instantiated model 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 29

  29. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 30

  30.  Transportation routines via Dynamic Bayes Nets  Significant places via Conditional Random Fields  Activity recognition projects  Discussion 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 31

  31. [Liao-Fox-Kautz: NIPS-05,IJCAI-05,IJRR-07]  So far No distinction between different types of goals Model not transferable to other person Goals learned solely based on duration 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 32

  32. Friend Restaurant Work Home Bus stop Parking Bus stop Store 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 33

  33.  Undirected graphical model  Introduced for labeling sequence data [Lafferty-et al: ICML-01]  No independence assumption on observations!  Trained discriminatively from labeled data  Applied successfully to Natural language processing [McCallum-Li: CoNLL-03], [Roth-Yih: ICML-05] Computer vision [Kumar-Hebert: NIPS-04], [Quattoni-Collins-Darrel: NIPS-05] Robotics [Limketkai-Liao-Fox: IJCAI-05], [Friedman-Pasula-Fox: IJCAI-07] 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 34

  34. ConditionalRandomFields x Hidden states x k 1  Observations z  Posterior factorizes into log-linear clique potentials 1 ( | ) ( , ( ) c C Z   z 1 ( ) z       T c c C   x z x z w x z ) exp f ( c , ) p    c c c c c Z  Local potentials link states to observations / features  Neighborhood potentials link states to neighboring states 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 35

  35. ConditionalRandomFields x Hidden states x k 1  Observations z  Posterior factorizes into log-linear clique potentials 1 ( | ) ( , ( ) c C Z   z 1 ( ) z       T c c C   x z x z w x z ) exp f ( c , ) p    c c c c c Z  Local potentials link states to observations / features  Neighborhood potentials link states to neighboring states 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 36

  36. ConditionalRandomFields x Hidden states x k 1  Observations z  Posterior factorizes into log-linear clique potentials 1 ( | ) ( , ( ) c C Z   z 1 ( ) z       T c c C   x z x z w x z ) exp f ( c , ) p    c c c c c Z Partition function Weights Feature functions  Local potentials link states to observations / features  Neighborhood potentials link states to neighboring states 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 37

  37.  Conditional distribution parameterized via weights w: 1 ( | , ) exp ( , ) c C Z   z w      T c x z w w x z f ( c , ) p  c c   Maximize conditional log-likelihood with shrinkage prior: T log ( | , ( ) ) L p  w w w x z w   2  Maximization via stochastic / conjugate gradient, L-BFGS  Alternative: maximize pseudo log-likelihood [Besag: 1975] T w w log (  w x x w PL( ) |MB( ), ) p   i i  2 i 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 38

  38.  BP computes posteriors via local message passing Sum-product for posterior .... Max-product for MAP ....  Exact if network has no loops  Otherwise, run loopy belief propagation and hope it works  Alternatives: ICM, graph-cut, MCMC, … 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 39

  39.  Temporal pattern: duration, time of day, etc.  Geographic evidence: is there a restaurant / store / bus stop nearby?  Transition relation: adjacent activities  Spatial feature: relation between place and activity  Summation constraint: number of places labeled home or workplace 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 40

  40. [Liao-Fox-Kautz: NIPS-05] Activity sequence walk, drive, ride bus, work, visit, sleep, pickup, get on/off bus … a1 a2 a3 a4 a5 aN-2 aN-1 aN … GPS trace 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 41

  41. [Liao-Fox-Kautz: NIPS-05] Global, soft constraints # homes, workplaces h w Significant places home, work, bus stop, parking lot, friend’s home … p1 p2 p3 pK Activity sequence walk, drive, ride bus, work, visit, sleep, pickup, get on/off bus … a1 a2 a3 a4 a5 aN-2 aN-1 aN … GPS trace  Two weeks data (> 50,000 gps points)  Inference: loopy BP (10 minutes); Learning: Pseudo-likelihood (3 minutes) 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 42

  42.  Generalize from people with labeled data to others without labeled data User1 Supervised learning Training data Generic model Other users User2 Shared parameters User3  Relational Markov Networks to specify CRFs and parameter sharing via templates [Taskar-Abbeel-Koller: UAI-02] 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 43

  43. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 44

  44. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 45

  45. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 46

  46. Cross-validation using data from 4 persons 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 47

  47. 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 48

  48.  Transportation routines via Dynamic Bayes Nets  Significant places via Conditional Random Fields  Activity recognition projects  Discussion 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 49

  49. [Ferris-Haehnel-Fox: RSS-06] 573 AU-08 Dieter Fox: Activity Recognition From Wearable Sensors 50

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