1. A Behavioral Pattern Adapted to Individual for Providing Ubiquitous Service in Intelligent Space yama@de.is.ritsumei.ac.jp WSEAS CIMMACS2006 in Venice Hiroyuki Yamahara Hideyuki Takada Hiromitsu Shimakawa Ritsumeikan University Nov. 20, 2006 simakawa@cs.ritsumei.ac.jp 1-1-1 Nojihigashi Kusatsu Shiga 525-8577 Japan htakada@cs.ritsumei.ac.jp

2. Assist user activity in ubiquitous environment  There are lots of researches to assist user activity in a ubiquitous environment. It is useful for solitary old people and two income family, who want help from outsiders.  It is desirable to be proactively provided services to assist user activity according to user intention.

3. Proactive services according to user behavior  Our research aim to provide proactive services according to user behavior which indicates user intention. It improves user amenity. It brings relief and safety to a user by preventing danger in advance.  Examples of service: a scene of going out To warn that a gas valve is not close To notify that a user left something behind To call an elevator to living floor beforehand

4. When does a user need assistance ?  A user does not need assistance in every scene of daily life.  It is desirable for a user to be assisted in a scene in which user ’ s mode significantly changes. Scenes of going out, coming home, getting up, going to bed Suppose you have gone out without closing a gas valve …

5. Provide service focusing on change of mode  We aim to detect user behavior in a scene in which user ’ s mode significantly changes, and provide services proactively before user ’ s mode has changed.  Improvement of user amenity in daily life  Proactive prevention of danger

6. Cycle of detection of user behavior behavior log behavioral pattern behavior log behavior log behavior log Time Passage Some behavior logs are collected as samples. A behavioral pattern is created with sample behavior logs. User behavior is detected by matching current behavior log with a behavioral pattern.

7. Existing behavior recognition method  Existing methods represent a behavioral pattern with a probabilistic model such as Hidden Markov Model. It represents user behavior as a series of state transition between two state. State 1State 2State 3State 4

8. Problems in existing method  They need a lot of sample behavior logs to create a behavioral pattern. It cannot realize early start of service to a user.  They cannot recognize user behavior which contains exceptionally rare actions. Exceptional rare actions are often weaved into actual user behavior in daily life.

9. Tagged World  An intelligent space to identify targets of user operation in daily life.  User ’ s behavior logs are collected with close-range RFID system. history of tag-ID of objects a user touched and its time stamp Tagged World

10. Individual habit represented by objects  A user has habitual actions individually in a scene in which his mode changes. Suppose a scene of going out …  pick up a wallet, go to the toilet, and so on Everyone often touches same objects in a scene.  Individual habit is represented by kind of objects a user touched and order of them.  The Tagged World creates an individual behavioral pattern from individual habit.

11. [case_ID : 1] 100000055 pants hanger 100000017 lavatory faucet 100000018 lavatory cup 100000020 toothbrush 100000019 toothpaste 100000020 toothbrush 100000018 lavatory cup 100000020 toothbrush 100000018 lavatory cup 100000017 lavatory faucet 100000068 cell phone 100000063 pass case 100000065 wrist watch 100000050 bag ・・・ [ordered pair] [count] pants hanger → lavatory faucet 9 pants hanger → lavatory cup 18 pants hanger → toothbrush 10 pants hanger → toothpaste 10 ・・・ p1: pants hanger → lavatory cup p2: pants hanger → cell phone p3: cell phone → milk carton p4: cell phone → bag p5: wrist watch → bag ・・・ (1)Collect behavior logs (2) Enumerate ordered pairs (3) Count occurrence (4) Ordered pairs more than the threshold are extracted Some cases of behavior logs Behavioral pattern represented by a set of ordered pairs How to create a behavioral pattern Ordered pair ： An order between two objects when a user touches an object B after he touches object A. ※ Two objects must not be successive.

12. p 1 : pants hanger → lavatory cup p 2 : pants hanger → cell phone p 3 : cell phone → milk carton p 4 : cell phone → bag p 5 : wrist watch → bag ・・・ 200000613 wardrobe 200000597 hanger 200000613 wardrobe 100000068 cell phone 100000055 pants hanger 100000017 lavatory faucet 100000018 lavatory cup 100000020 toothbrush 100000019 toothpaste 100000020 toothbrush 100000018 lavatory cup 100000020 toothbrush 100000018 lavatory cup 100000020 toothbrush 100000018 lavatory cup 100000017 lavatory faucet 100000071 refrigerator 100000072 milk carton 100000071 refrigerator ・・・ ・・・ ordered pair number of occurrence p 1 : pants hanger → lavatory cup4 p 2 : pants hanger → cell phone0 p 3 : cell phone → milk carton1 ・・・ calculate the characteristic point according to the number of occurrence of each ordered pair Behavior log to be matchedBehavioral pattern represented by a set of ordered pairs How to match with a behavioral pattern

13. Features in the proposed method  Representation of a behavioral pattern with not only successive two objects but also non-successive two objects in a behavior log  Separation of probabilistic model from order check in matching with a behavioral pattern These features can flexibly characterize user behavior of each scene. Our method isn't affected by exceptional rare actions weaved into actual user behavior.

14. Experiment  Target scene to detect Going out (before he has gone through the entrance door)  Scenes of collected behavior logs as samples to create a behavioral pattern  Going out as logs to be matched with the pattern  Going out, Coming home, and so on.  A behavioral pattern is created with 3 to 10 sample behavior logs.  with 15 experimental subjects

15. Recognition rate The number of sample behavior logs Detection rate of “ going out ” Exclusion rate except “ going out ”

16. Discussion  A behavioral pattern of going out detected 98% of user behavior of going out excluded 95% of user behavior in other scenes  A behavioral pattern of coming home detected 93% of user behavior of coming home excluded 92% of user behavior in other scenes Less objects are touched in a scene of coming home compared to a scene of going out. There are less characteristics of user behavior.  The experiment has proved our method can create a behavioral pattern with less than 10 behavior logs in a practical short time.

17. Conclusion  The Tagged World aims to provide services proactively before user ’ s mode has changed, by detecting user behavior in a scene in which user ’ s mode significantly changes.  Our method individualizes user behavior with order of objects a user touches in every scene, and starts service to a user by creating an individual behavioral pattern in a short time, and detects user behavior without being affected by rare order of user ’ s action.  In the future, we will let a portable computer collaborate closely with living space information to provide more effective and appropriate services.