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Spatio-Temporal and Context Reasoning in Smart Homes

Spatio-Temporal and Context Reasoning in Smart Homes. Sook-Ling (Linda) Chua Stephen Marsland, Hans W. Guesgen. COSIT 2009. School of Engineering and Advanced Technology Massey University, New Zealand. T. h e S i t u a t i o n. . . . . The world is aging. - have we noticed?.

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Spatio-Temporal and Context Reasoning in Smart Homes

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  1. Spatio-Temporal and Context Reasoningin Smart Homes Sook-Ling (Linda) Chua Stephen Marsland, Hans W. Guesgen COSIT 2009 School of Engineering and Advanced Technology Massey University, New Zealand

  2. T h e S i t u a t i o n . . .     The world is aging - have we noticed? Source: United Nations (2007)

  3. T h e S i t u a t i o n . . .     • The populations of the world are aging Source: United Nations (2007)

  4. T h e S i t u a t i o n . . .     • The populations of the world are aging Source: United Nations (2007)

  5. T h e S i t u a t i o n . . .     • The populations of the world are aging > 25% < 10% Source: United Nations (2007)

  6. T h e S i t u a t i o n . . .     • People choose to stay in their ownhomes as long as possible and remain independent

  7. T h e S i t u a t i o n . . .     • People choose to stay in their ownhomes as long as possible and remain independent Aging leads to Physical disability Cognitive impairment • diminished sense and touch • slower ability to react • poor vision, hearing problems • memory problems

  8. T h e S i t u a t i o n . . .     • Supporting inhabitant’s daily activities • “Smart Homes” Figure extracted from:http://www.dreamhomesmagazine.com/

  9. T h e S i t u a t i o n . . .     • To react intelligently, the smart home needs to: (1) recognise inhabitant’s behaviour (2) perform reasoning • spatio-temporalinformation • contextual information

  10. B “Tokens” e h a v i o u r R e c o g n i t i o n     sensoroutput The Smart Home Figure extracted from:http://www.dreamhomesmagazine.com/

  11. B “Tokens” e h a v i o u r R e c o g n i t i o n     The direct representation of current sensor states being triggered E.g. of a sequence of tokens from the sensors

  12. B “Tokens” e h a v i o u r R e c o g n i t i o n     The direct representation of current sensor states being triggered E.g. of a sequence of tokens from the sensors

  13. B 19th Jan 2009 18:03:16 … 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 19th Jan 2009 19:41:26 . . . . . . e h a v i o u r R e c o g n i t i o n     Office/Study Bedroom Laundry Dining/Living Room Kitchen Figure extracted from: The Aware Home, 2002

  14. B 19th Jan 2009 18:03:16 … 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 19th Jan 2009 19:41:26 . . . . . . e h a v i o u r R e c o g n i t i o n     Office/Study Bedroom Laundry Dining/Living Room Kitchen Figure extracted from: The Aware Home, 2002

  15. B 19th Jan 2009 18:03:16 … 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 19th Jan 2009 19:41:26 . . . . . . e h a v i o u r R e c o g n i t i o n     Office/Study Bedroom Q: How do we recognise behaviours? Laundry Dining/Living Room Kitchen Figure extracted from: The Aware Home, 2002

  16. B Office/Study Bedroom Laundry Dining/Living Room Kitchen e h a v i o u r R e c o g n i t i o n     • Challenges: (a) Exact activities are not directly observed, only the sensor observations 19th Jan 2009 18:03:16 . . . 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 . . . . . . Figure extracted from: The Aware Home, 2002

  17. B Office/Study Bedroom Laundry Dining/Living Room Kitchen e h a v i o u r R e c o g n i t i o n     • Challenges: (a) Exact activities are not directly observed, only the sensor observations 19th Jan 2009 18:03:16 . . . 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 . . . . . . Figure extracted from: The Aware Home, 2002

  18. B Office/Study Bedroom ? Laundry Dining/Living Room Kitchen e h a v i o u r R e c o g n i t i o n     • Challenges: (a) Exact activities are not directly observed, only the sensor observations 19th Jan 2009 18:03:16 . . . 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 19th Jan 2009 19:37:21 . . . . . . Figure extracted from: The Aware Home, 2002

  19. B e h a v i o u r R e c o g n i t i o n     • Challenges: (b) Same sensor activations will be involved in multiple behaviours

  20. B e h a v i o u r R e c o g n i t i o n     • Challenges: (b) Same sensor activations will be involved in multiple behaviours Figure extracted from: www.rebecca-waring.com, www.cyh.com, www.chow.com

  21. B e h a v i o u r R e c o g n i t i o n     • Challenges: (c) No. of observations can vary between activities Making breakfast Making dinner Microwave Oven Fridge Toaster Stove Cupboard Cupboard Drawer Tap

  22. B e h a v i o u r R e c o g n i t i o n     • Challenges: (d) Behaviours are rarely identical on each use • components can be present/absent • the order of individual components happen can change • length of time each piece takes can change E.g. Making a cup of tea With / without Milk / water first? How long?

  23. B e h a v i o u r R e c o g n i t i o n     • Challenges: (d) Behaviours are rarely identical on each use • components can be present/absent • the order of individual components happen can change • length of time each piece takes can change Stochastic Approach E.g. Making a cup of tea With / without Milk / water first? How long?

  24. B e h a v i o u r R e c o g n i t i o n     The Hidden Markov Model (HMM) • probabilistic graphical model • uses probability distributions to determine the states for a sequence of observations over time Source: Rabiner, L. (1989)

  25. B e h a v i o u r R e c o g n i t i o n     The Hidden Markov Model (HMM) • probabilistic graphical model • uses probability distributions to determine the states for a sequence of observations over time Observations We know this.. Source: Rabiner, L. (1989)

  26. B … … States But, not this e h a v i o u r R e c o g n i t i o n     The Hidden Markov Model (HMM) • probabilistic graphical model • uses probability distributions to determine the states for a sequence of observations over time Observations We know this.. Source: Rabiner, L. (1989)

  27. B e h a v i o u r R e c o g n i t i o n …     The Hidden Markov Model (HMM) … … States Observations • Markov property: • The probability of transition to a state (St+1) depends only on the current state (St) [represented by solid line] • The observation at Ot depends only on the state St at that time slice [represented by dashed line] Source: Rabiner, L. (1989)

  28. B e h a v i o u r R e c o g n i t i o n …     The Hidden Markov Model (HMM) … … States Observations • Markov property: • The probability of transition to a state (St+1) depends only on the current state (St) [represented by solid line] • The observation at Ot depends only on the state St at that time slice [represented by dashed line] Source: Rabiner, L. (1989)

  29. B e h a v i o u r R e c o g n i t i o n …     The Hidden Markov Model (HMM) … … States Observations • Markov property: • The probability of transition to a state (St+1) depends only on the current state (St) [represented by solid line] • The observation at Ot depends only on the state St at that time slice [represented by dashed line] Source: Rabiner, L. (1989)

  30. B Office/Study Bedroom Laundry Dining/Living Room Kitchen e h a v i o u r R e c o g n i t i o n     19th Jan 2009 18:03:16 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 . . . . . .

  31. B Office/Study Bedroom Laundry Dining/Living Room Kitchen Observations e h a v i o u r R e c o g n i t i o n     19th Jan 2009 18:03:16 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 . . . . . .

  32. B Office/Study Bedroom ? Laundry Coffee Machine Cupboard Fridge States Dining/Living Room Kitchen Observations e h a v i o u r R e c o g n i t i o n     19th Jan 2009 18:03:16 19th Jan 2009 18:07:56 19th Jan 2009 18:20:27 19th Jan 2009 18:33:44 19th Jan 2009 18:50:12 19th Jan 2009 19:01:08 . . . . . .

  33. B e h a v i o u r R e c o g n i t i o n     • To use HMM to recognise behaviours: (1)Segmentation break the token sequence into appropriate pieces that represent individual behaviours start end start end Observations . . .

  34. B e h a v i o u r R e c o g n i t i o n     • To use HMM to recognise behaviours: (2) Classification identify the behaviours using the HMM . . . Observations “Behaviour A” “Behaviour B”

  35. B e h a v i o u r R e c o g n i t i o n     Behaviour Recognition using HMM • Our approach: • Use a set of HMMs that each recognise different behaviours . . . “Making lunch” “Making coffee” “Showering” • These HMMs will compete to explain the current observations • Model selection is based on maximum likelihood Source: Chua, Marsland and Guesgen (2009)

  36. B e h a v i o u r R e c o g n i t i o n     Experiment: Competition between HMMs • Datasets • MIT PlaceLab • Designed a set of simply installed state-change sensors that were placed in two different apartments with real people living in them Source: Tapia (2004)

  37. B e h a v i o u r R e c o g n i t i o n     Experiment: Competition between HMMs • Datasets • The subjects kept a record of their activities that form a set of annotations for the data  “Ground-truth” segmentation of the dataset • We used the dataset from the first subject • 77 sensors • collected for 16 consecutive days

  38. B e h a v i o u r R e c o g n i t i o n     Experiment: Competition between HMMs • Datasets • Activities take place in one room (kitchen) • Location of the sensors is known a priori • Behaviours: • Prepare breakfast (toaster) • Prepare breakfast (cereal) • Prepare beverage • Prepare lunch • Do the laundry

  39. B e h a v i o u r R e c o g n i t i o n     Based on 727 observations (using 11 days testing and 5 days training set)

  40. B e h a v i o u r R e c o g n i t i o n     Based on 727 observations (using 11 days testing and 5 days training set)

  41. B e h a v i o u r R e c o g n i t i o n     Based on 727 observations (using 11 days testing and 5 days training set)

  42. B e h a v i o u r R e c o g n i t i o n     Experimental Results • Method works effectively • performs segmentation and detects changes of activities . . . observation Coffee Machine Drawer Drawer Microwave Fridge Cupboard Fridge

  43. B e h a v i o u r R e c o g n i t i o n     Experimental Results • Method works effectively • performs segmentation and detects changes of activities Preparing a beverage Preparing lunch . . . observation Coffee Machine Drawer Drawer Microwave Fridge Cupboard Fridge

  44. B e h a v i o u r R e c o g n i t i o n     • Discussion Lack of spatio-temporal information • Misclassification:  The end of one behaviour contains observations that should be the start of the next Preparing a beverage Preparing lunch … Drawer Coffee Machine Drawer Microwave Cupboard Fridge Fridge observation

  45. B e h a v i o u r R e c o g n i t i o n     • Discussion Lack of spatio-temporal information • Misclassification:  The end of one behaviour contains observations that should be the start of the next Preparing a beverage Preparing lunch … Drawer Coffee Machine Drawer Microwave Cupboard Fridge Fridge observation

  46. B e h a v i o u r R e c o g n i t i o n     • Discussion Lack of spatio-temporal information • Misclassification:  The end of one behaviour contains observations that should be the start of the next Preparing a beverage Preparing lunch … Drawer Coffee Machine Drawer Microwave Cupboard Fridge Fridge observation

  47. B Preparing a beverage Preparing lunch e h a v i o u r R e c o g n i t i o n     • Discussion Lack of spatio-temporal information • Misclassification:  The end of one behaviour contains observations that should be the start of the next Preparing a beverage Preparing lunch . . . Drawer Coffee Machine Drawer Microwave Cupboard Fridge Fridge observation

  48. B e h a v i o u r R e c o g n i t i o n     • Discussion Lack of spatio-temporal information Q: How to reduce misclassification? A: Augment current algorithm to include spatio-temporal information

  49. S p a t i o - t e m p o r a l     • Spatial information (Where?) • NOT directly interested in the exact coordinates • So, what are we interested in?  Room location  e.g. Figures extracted from: www.istockphoto.com, www.clubjam.jammag.com, www.nancilea.blogspot.com

  50. S p a t i o - t e m p o r a l     • Spatial information (Where?) • Current study used very basic spatial information (just the kitchen!) • In the future, . . .

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