Discrete Events Modelling of a Person Behaviour at Home

Transcription

1 FMH606 Master's Thesis 2017 Industrial IT and Automa on Discrete Events Modelling of a Person Behaviour at Home Badreddine Cherradi Faculty of Technology, Natural Sciences and Mari me Sciences Campus Porsgrunn

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5 Preface

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7 Contents Preface 5 Contents 8 1 Introduc on 15 2 Theore cal background 21 3 Methodology 33

8 4 Results 53 5 Future work 59 6 Conclusion 63 References 65 Appendices 69

9 List of Figures

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11 List of Tables

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13 Nomenclature

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15 1 Introduc on

16 1.1 Project Descrip on

17 The sensor detects the entrance in the Main Hall Sensor The sensor detects the entrance in the Bedroom Main hall The sensor detects the entrance in the Bathroom Bedroom Bathroom The sensor detects a presence in front of the sink

18 Activity ROOM POSITION TIME Unknown Unknown : 26 : 45 Hall Standing : 27 : 00 Hall Standing : 27 : 15 Bedroom Standing : 27 : 30 Bathroom Standing : 27 : 45 Bathroom Standing : 28 : 00 Bathroom Standing : 28 : 15 Simple behavior Complex behavior

19 1.2 Document structure

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21 2 Theore cal background 2.1 Related Work Hidden Markov Models

22 2.1.2 Fuzzy Logic

23 2.1.3 Frequent Pa ern Mining e 1 e 2 e m Recurrent Neural Network

24 2.2 Stochas c methods for discrete events modelling p 1 p T 1 + t 1 T 2 + t 2 t 1 t Hidden Markov Models N S = {S 1 S 2 S N } M O = {O 1 O 2 O M } X t t A = {a i j } a i j = P(X t+1 = S j X t = S i ) t B = {b i j } b j (k) = P(O k X t = S j )k [1,M] X t t X(t) {X 1,X 2 } Y (t) {y 1,y 2,y 3 } a i j b i j

25 a 12 X 1 X 2 a 21 b 11 b 12 b 13 b 21 b 22b 23 y 1 y 2 y 3 S 1 S 2 y 1 y 2 y 3 X 3 t = 3 P(X 1 ) P(S 1 ) P(S 2 ) P(X 3 y 1:3 ) P(S 1 ) P(S 2 ) X 1 X 2 X 3 y 1 y 2 y 3

26 t=1 t=2 t=3 y 1 y 2 y 3 Init S 1 S 1 S 1 S 2 S 2 S 2

27 2.2.2 Agent Reac ve Agents Sensor Agent Inputs Rules Action

28 Cogni ve Agents Agent Desires Beliefs Knowledge of the environment Sensor Inputs Filter Intentions Rules Environment Action

29 Mul -Agent System MAS monitoring simple behaviours

30 Information about this activity E.g. «This activity occurs in average n time during the day» Database Agent A 1 Activity 1 Real-time Behavior 1 t 2start Behavior 2 t 2end Behavior 3 t 1start t 1end t 3start t 3end Agent A 2 Durations Information about the time shift of behavior 3 E.g. «Knowing that behavior 1 and behavior 2 are taking longer this 2 last days, behavior 3 may start at x» Fuzzy Logic

31 Fuzzy set Linguis c Variables Fuzzy Rules FuzzySet Opera ons µ A B (x) µ A B (x) µ A (x) µ A (x) µ B (x) µ A (x) µ B (x) µ A (x)

32 Fuzzy inference systems (Mamdani) Numerical input 0.28 Numerical input 0.7 Rule Strength Membership 1,0 degree 0,8 Membership 1,0 degree 0,8 Membership 1,0 degree 0,8 Rules 1 0,6 0,4 0,2 0,6 OR 0,4 0,2 MAX 0,6 0,4 0,2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 LV1 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 LV2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 LV3 Membership 1,0 degree Membership 1,0 degree Membership 1,0 degree 0,8 0,8 0,8 Rule 2 0,6 0,6 0,6 0,4 0,2 0,4 AND 0,2 MIN 0,4 0,2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 LV1 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 LV2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,0 LV3 0,8 0,6 Output distribution 0,4 0,2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0

33 3 Methodology 3.1 Introduc on 3.2 Development Process Waterfall Requirements Design Requirements Evaluation Design Scrum Code Code Integration/Testing Integration/Testing Iteration 1 Requirements Evaluation Design Code Deployement Integration/Testing Iteration 2 Continue

34 3.3 States Position Room LOA RoomSection Undefined Lying Sitting Standing None Undefined Bedroom Livingroom Bathroom Kitchen None Undefined Low Medium High None Undefined Door Chair Bed Sink Toilet None

35 3.4 Simula ng a morning rou ne Ac vity and Behavior Activity 1. Lying, Bedroom, Low, Bed 2. Standing, Bedroom, Medium, Door 3. Standing, Livingroom, Medium, Door Time [s] [400 ; 430] [2 ; 4] [2 ; 4] Instance of the class Human Integer between 2 and 4

36 3.4.2 Database Timer

37 Behavior tick counter = Random behavior's time The simulator switches and populates the database with the next behavior Number of behavior counter = Number of behavior in the routine The simulator switches to the next day

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39 3.4.4 Codifica on Undefined Lying Sitting Standing None Position Varia on in the rou ne LIVING ROOM Lying Sitting Standing BEDROOM Lying Sitting Standing BATHROOM Sitting Standing

40 Activity Time 1. Lying, Bedroom, Low, Bed [200 ; 220] 2. Standing, Bathroom, Medium, Toilet [10 ; 20] 3 Lying, Bedroom, Low, Bed [200 ; 210] 4. Standing, Bedroom, Medium, Door [2 ; 4] Next possible room Bedroom Bathroom Livingroom Next possible position Next possible roomsection 5. Standing, Livingroom, Medium, Door [2 ; 4] Next possible level of activity

41 3.4.6 Cache database

42 3.5 Analysis Introduc on User Interface

43 Listbox of the Position status Enumerator Position from the class Human EventHandler

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45 3.5.4 Agents Agent-oriented programming

46 Service-oriented architecture

47 Our approach Survival analysis - Exponen al Distribu on

48 λ F(t) = P(X t) = 1 e λt S(t) = 1 F(t) = P(T > t) = e λt λ 1/λ λ 5

49 λ λ

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51 3.5.5 Classifica on

52 Membership degree 1,0 0,8 0,6 0,4 Bad Normal Good 0,2 0,0 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 Probability of survival

53 4 Results 4.1 Tes ng module

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55 4.2 Situa on 1 : Normal rou ne

56 4.3 Situa on 2 : Irrelevant behaviour me

57 4.4 Situa on 3 : Abnormal behaviour me Two weeks routine One day

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59 5 Future work Behaviour 3 Simulator Behaviour 2 Behaviour 1 Database new entry in the DB Analysis program EventHandler B1 Time Average Time Behavior 1 Survival probability Agent Average survival probability Fuzzy Rules Behavior 3 Agent Behavior 2 Agent Alert output 5.1 User-friendly rou ne adding system

60 5.2 Sequences analysis

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62 5.3 Frequency of behaviour occurrence

63 6 Conclusion

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65 References

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69 Appendices

70 Appendix 1: Project task description 70

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