SlideShare a Scribd company logo
1 of 21
Download to read offline
Leveraging Complex Event Processing
for Dog Behavior Monitoring
through Wireless Wearable Sensors
When Home Alone
THESIS DEFENSE
DIANA OVIEDO QUEVEDO
SOFTWARE ENG. LAB.
DECEMBER 7, 2016
Contents
Introduction
Methods
Results Analysis
Conclusions
Demo
2
Why Monitoring Pets?
3
[9] Video analysis of adult
dogs when left home alone
The overall Pet Monitoring Project
4
Pet Monitoring System Components
5
What is Complex Event Processing?
Computing that performs operations on events.
Operations on events such as:
 Filtering out certain events,
 Changing an event instance from one form to another,
 Examining a collection of events to find a particular pattern.
6
CEP
?
Event Consumers
Event Producers
Methods
7
Pattern Rules Hierarchical Structure
Level2Level3Level1
Loud Vocalization
Climbing
(Unwanted Behavior)
Destructive Behavior
(Separation Anxiety)
Vocalization
(Activity)
Jump
Up
Jump
Down
Stand in
2 legs
Walking
Engage
Object
Food Stealing
(Unwanted Behavior)
Sniffing
(Activity)
Head
Down
Barking Howling Whining Digging
Urination/
Defecation
1. Scope for the dog’s behavior events and its hierarchical level classification
Classification
process
How to Detect the Higher-Level Behavior?
8
Element Declarations
Variables String symbol,String event,Calendar timestamp, int id
Event types
BasicBehavior(id, symbol, timestamp),
HigherEvent(event,timestamp,id,level)
Pattern
every (Level2stream(event in('CL','SF')) and (BasicBehavior(symbol in
('EO','UD')) or [3]BasicBehavior(symbol='DI'))
Context
Condition
timer:within(3 min)
Action notifies destructiveBehavior(event,timestamp,id,3)
The Implementation: Class Diagram
9
The Implementation: Sequence Diagram
10
How one Level 3 Event is Generated
11
Level3
Level 1
Level 2
JU
HD
WA
Sniffing
HD
WA
Sniffing
EO
JD
Climbing
Food Stealing
Time in mm:ss.SS
FOOD STEALING EVENT GENERATION (IN 01:45.20 )
Results for Dog 2
12
Climbing
Sniffing
Food Stealing
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
BEHAVIORID
TIME ( MM:SS.00 )Level 1 Level 2 Level 3
Total time: 9 min 22 sec
L3
L1
L2
Results for Dog 3
13
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
BEHAVIORID
TIME ( MM:SS.00 )Level 1 Level 2 Level 3
Loud Vocalization
Destructive Behavior
Food Stealing
L3
L1
L2
Total time: 30 min 46 sec
Monitoring Reports Web App
14
PetMonitoring
DB
Results Analysis
 Performance
CEP in ESPER has shown in previous tests that it supports up to 500.000 events/s.
The purpose of CEP for this research is mainly to detect behavior patterns in real
time, and so far we only have a maximum of 3 events per second.
 Concurrency and overhead
Were handled through Inbound threading. It was implemented in the configuration
of the ESPER engine, which allows to handle it in an engine-level manner, instead
of the (system) time-based processing by default.
 Accuracy
15
Dog 1 Dog 2 Dog 3
Total detected events 12 20 152
Expected events 13 20 151
Accuracy 92.31% 100 % 100 %
Results Analysis (2)
 Latency
In Level 3 events compared to the last
previous event needed to match the
pattern
16
Food Stealing Loud Vocalization
Destructive
Behavior
Behavior ID 30 31 32
Max 1.160E-06 7.990E-06 1.273E-05
Average 1.160E-06 2.420E-06 2.080E-06
Min 1.160E-06 0.000E+00 0.000E+00
0.000E+00
2.000E-06
4.000E-06
6.000E-06
8.000E-06
1.000E-05
1.200E-05
1.400E-05
30 31 32
Max Average Min
Demo…
17
Conclusions and Future Work
 Complex Event Processing can represent a significant contribution to the monitoring
of dog’s behavior when left home alone, parting from basic behavior inputs, higher-
level behavior events can be detected in order to produce only the adequate
amount of notifications to the owner.
 The application of CEP for the detection of behavior events can be interpreted as a
kind of middleware application within a bigger IoT system. When integrated
provides a full service for taking care of dogs home alone.
 The behavior monitoring can be further extended to a broader range of higher-level
pattern rules, including the prediction of unwanted behavior or diagnosis of
separation anxiety problems. It also can be extended for other animals or more than
one animal simultaneously.
18
References
1. Konok V, Dóka A, Miklósi Á. The behavior of the domestic dog (Canis familiaris) during
separation from and reunion with the owner: A questionnaire and an experimental study.
Applied Animal Behaviour Science, 2011, 135(4): 300-308.
2. Frank D., Minero M., Cannas S., Palestrini C. Puppy behaviours when left home alone: A
pilot study. Applied Animal Behaviour Science, 2007, 104, 61-701.
3. Lund J D, Jørgensen M C. Behaviour patterns and time course of activity in dogs with
separation problems. Applied Animal Behaviour Science, 1999, 63(3): 219-236.
4. Lukham, David. 2002. The Power of Events: An Introduction to Complex Event
Processing in Distributed Enterprise Systems. Addison Wesley.
5. Bhargavi.R, Vaidehi. Semantic intrusion detection with multisensor data fusion using
complex event processing. Sadhana Indian Academy of Science, 2013, 38(2), 169-185.
6. Hänninen L1, Pastell M.CowLog: open-source software for coding behaviors from digital
video. Behav Res Methods, 2009 May;41(2):472-6
7. Etzion, Opher and Niblett, Peter. 2011. Event Processing in Action. Manning.
8. Vaidehi V, Bhargavi R, Ganapathy K, et al. Multi-sensor based in-home health
monitoring using complex event processing. Recent Trends in Information Technology
(ICRTIT), 2011 International Conference on. IEEE, 2011: 570-575.
19
References
9. Scaglia E, Cannas S, Minero M, et al. Video analysis of adult dogs when left home alone. Journal of
Veterinary Behavior: Clinical Applications and Research, 2013, 8(6): 412-417.
10. Tae-ho Chung, Chul Park, Yong-man Kwon, and Seong-chan Yeon, “Prevalence of canine behavior
problems related to dog-human relationship in South Korea – A pilot study”, Journal of Veterinary
Behavior, 11 (2016) pp. 26-30.
11. Software Engineering Lab, Korea University, 2016. Pets Management System Based on IoT for
Improvement of Living Conditions.
12. Moshnyaga V, Osamu T, Ryu T, Hashimoto K “Identification of Basic Behavioral Activities by
Heterogeneous Sensors of In-Home Monitoring System”. 6th International Workshop, HBU 2015. 160-
174.
13. Martiskainen, Paula, et al. "Cow behaviour pattern recognition using a three-dimensional
accelerometer and support vector machines." Applied Animal Behaviour Science 119.1(2009):32-38.
14. Lianli Gao, Hamish A. Campbell, Owen R. Bidder, Jane Hunter. Corrigendum to “A web-based
semantic tagging and activity recognition system for species' accelerometry data” [Ecol. Inf. 13 (2013)
47–56]. Ecological Informatics, Volume 22, July 2014, Page 81.
15. Esper Team and EsperTech Inc. “Esper reference”. Version 5.2.0
20
Thank you.
21

More Related Content

Similar to ThesisDefenseDOQ_12-2016

Animal Breed Classification And Prediction Using Convolutional Neural Network...
Animal Breed Classification And Prediction Using Convolutional Neural Network...Animal Breed Classification And Prediction Using Convolutional Neural Network...
Animal Breed Classification And Prediction Using Convolutional Neural Network...Allison Thompson
 
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
 
ppt(ANIMAL MONITORING).pptx
ppt(ANIMAL MONITORING).pptxppt(ANIMAL MONITORING).pptx
ppt(ANIMAL MONITORING).pptxAashukumariSingh
 
Vaataja 2012 ux technology mediated interaction humans-dogs
Vaataja 2012   ux technology mediated interaction humans-dogsVaataja 2012   ux technology mediated interaction humans-dogs
Vaataja 2012 ux technology mediated interaction humans-dogsHeli Väätäjä
 
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...InsideScientific
 
Ieeepro techno solutions 2013 ieee embedded project - child activity recog...
Ieeepro techno solutions   2013 ieee embedded project  - child activity recog...Ieeepro techno solutions   2013 ieee embedded project  - child activity recog...
Ieeepro techno solutions 2013 ieee embedded project - child activity recog...srinivasanece7
 
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODS
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODSA BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODS
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODSsipij
 
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING ijujournal
 
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...ijujournal
 
Sensor based Health Monitoring System
Sensor based Health Monitoring System Sensor based Health Monitoring System
Sensor based Health Monitoring System Sudhanshu Janwadkar
 
Iaetsd an effective alarming model for danger and activity
Iaetsd an effective alarming model for danger and activityIaetsd an effective alarming model for danger and activity
Iaetsd an effective alarming model for danger and activityIaetsd Iaetsd
 
Mobile-based monitoring system for an automatic cat feeder using Raspberry Pi
Mobile-based monitoring system for an automatic cat feeder using Raspberry PiMobile-based monitoring system for an automatic cat feeder using Raspberry Pi
Mobile-based monitoring system for an automatic cat feeder using Raspberry PiTELKOMNIKA JOURNAL
 
Information processing in betting and investing behavior
Information processing in betting and investing behaviorInformation processing in betting and investing behavior
Information processing in betting and investing behaviorAlessandro Innocenti
 
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...Daniel Katz
 
The Importance Of Physical Evidence
The Importance Of Physical EvidenceThe Importance Of Physical Evidence
The Importance Of Physical EvidenceAlicia Johnson
 
How to investigate behavior and cognitive abilities in rodents in a social gr...
How to investigate behavior and cognitive abilities in rodents in a social gr...How to investigate behavior and cognitive abilities in rodents in a social gr...
How to investigate behavior and cognitive abilities in rodents in a social gr...InsideScientific
 
A Survey on Various Animal Health Monitoring and Tracking Techniques
A Survey on Various Animal Health Monitoring and Tracking TechniquesA Survey on Various Animal Health Monitoring and Tracking Techniques
A Survey on Various Animal Health Monitoring and Tracking TechniquesIRJET Journal
 

Similar to ThesisDefenseDOQ_12-2016 (20)

Animal Breed Classification And Prediction Using Convolutional Neural Network...
Animal Breed Classification And Prediction Using Convolutional Neural Network...Animal Breed Classification And Prediction Using Convolutional Neural Network...
Animal Breed Classification And Prediction Using Convolutional Neural Network...
 
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...
 
ppt(ANIMAL MONITORING).pptx
ppt(ANIMAL MONITORING).pptxppt(ANIMAL MONITORING).pptx
ppt(ANIMAL MONITORING).pptx
 
Vaataja 2012 ux technology mediated interaction humans-dogs
Vaataja 2012   ux technology mediated interaction humans-dogsVaataja 2012   ux technology mediated interaction humans-dogs
Vaataja 2012 ux technology mediated interaction humans-dogs
 
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...
Case Studies in Home Cage Monitoring: Rodent Behavior, Circadian Biology and ...
 
Ieeepro techno solutions 2013 ieee embedded project - child activity recog...
Ieeepro techno solutions   2013 ieee embedded project  - child activity recog...Ieeepro techno solutions   2013 ieee embedded project  - child activity recog...
Ieeepro techno solutions 2013 ieee embedded project - child activity recog...
 
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODS
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODSA BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODS
A BRIEF OVERVIEW ON DIFFERENT ANIMAL DETECTION METHODS
 
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING
DTWDIR: AN ENHANCED DTW ALGORITHM FOR AUTISTIC CHILD BEHAVIOUR MONITORING
 
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...
https://www.academia.edu/81610651/FURTHER_INVESTIGATIONS_ON_DEVELOPING_AN_ARA...
 
Electronic Ear Tag for Farm Animals
Electronic Ear Tag for Farm AnimalsElectronic Ear Tag for Farm Animals
Electronic Ear Tag for Farm Animals
 
Behavioral Monitoring Tool for Pigs
Behavioral Monitoring Tool for PigsBehavioral Monitoring Tool for Pigs
Behavioral Monitoring Tool for Pigs
 
Sensor based Health Monitoring System
Sensor based Health Monitoring System Sensor based Health Monitoring System
Sensor based Health Monitoring System
 
Iaetsd an effective alarming model for danger and activity
Iaetsd an effective alarming model for danger and activityIaetsd an effective alarming model for danger and activity
Iaetsd an effective alarming model for danger and activity
 
Mobile-based monitoring system for an automatic cat feeder using Raspberry Pi
Mobile-based monitoring system for an automatic cat feeder using Raspberry PiMobile-based monitoring system for an automatic cat feeder using Raspberry Pi
Mobile-based monitoring system for an automatic cat feeder using Raspberry Pi
 
Information processing in betting and investing behavior
Information processing in betting and investing behaviorInformation processing in betting and investing behavior
Information processing in betting and investing behavior
 
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...
ICPSR - Complex Systems Models in the Social Sciences - Lab Session 5 - Profe...
 
The Importance Of Physical Evidence
The Importance Of Physical EvidenceThe Importance Of Physical Evidence
The Importance Of Physical Evidence
 
Reiter lecture 11.11.14
Reiter lecture 11.11.14Reiter lecture 11.11.14
Reiter lecture 11.11.14
 
How to investigate behavior and cognitive abilities in rodents in a social gr...
How to investigate behavior and cognitive abilities in rodents in a social gr...How to investigate behavior and cognitive abilities in rodents in a social gr...
How to investigate behavior and cognitive abilities in rodents in a social gr...
 
A Survey on Various Animal Health Monitoring and Tracking Techniques
A Survey on Various Animal Health Monitoring and Tracking TechniquesA Survey on Various Animal Health Monitoring and Tracking Techniques
A Survey on Various Animal Health Monitoring and Tracking Techniques
 

ThesisDefenseDOQ_12-2016

  • 1. Leveraging Complex Event Processing for Dog Behavior Monitoring through Wireless Wearable Sensors When Home Alone THESIS DEFENSE DIANA OVIEDO QUEVEDO SOFTWARE ENG. LAB. DECEMBER 7, 2016
  • 3. Why Monitoring Pets? 3 [9] Video analysis of adult dogs when left home alone
  • 4. The overall Pet Monitoring Project 4
  • 5. Pet Monitoring System Components 5
  • 6. What is Complex Event Processing? Computing that performs operations on events. Operations on events such as:  Filtering out certain events,  Changing an event instance from one form to another,  Examining a collection of events to find a particular pattern. 6 CEP ? Event Consumers Event Producers
  • 7. Methods 7 Pattern Rules Hierarchical Structure Level2Level3Level1 Loud Vocalization Climbing (Unwanted Behavior) Destructive Behavior (Separation Anxiety) Vocalization (Activity) Jump Up Jump Down Stand in 2 legs Walking Engage Object Food Stealing (Unwanted Behavior) Sniffing (Activity) Head Down Barking Howling Whining Digging Urination/ Defecation 1. Scope for the dog’s behavior events and its hierarchical level classification Classification process
  • 8. How to Detect the Higher-Level Behavior? 8 Element Declarations Variables String symbol,String event,Calendar timestamp, int id Event types BasicBehavior(id, symbol, timestamp), HigherEvent(event,timestamp,id,level) Pattern every (Level2stream(event in('CL','SF')) and (BasicBehavior(symbol in ('EO','UD')) or [3]BasicBehavior(symbol='DI')) Context Condition timer:within(3 min) Action notifies destructiveBehavior(event,timestamp,id,3)
  • 11. How one Level 3 Event is Generated 11 Level3 Level 1 Level 2 JU HD WA Sniffing HD WA Sniffing EO JD Climbing Food Stealing Time in mm:ss.SS FOOD STEALING EVENT GENERATION (IN 01:45.20 )
  • 12. Results for Dog 2 12 Climbing Sniffing Food Stealing 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 BEHAVIORID TIME ( MM:SS.00 )Level 1 Level 2 Level 3 Total time: 9 min 22 sec L3 L1 L2
  • 13. Results for Dog 3 13 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 BEHAVIORID TIME ( MM:SS.00 )Level 1 Level 2 Level 3 Loud Vocalization Destructive Behavior Food Stealing L3 L1 L2 Total time: 30 min 46 sec
  • 14. Monitoring Reports Web App 14 PetMonitoring DB
  • 15. Results Analysis  Performance CEP in ESPER has shown in previous tests that it supports up to 500.000 events/s. The purpose of CEP for this research is mainly to detect behavior patterns in real time, and so far we only have a maximum of 3 events per second.  Concurrency and overhead Were handled through Inbound threading. It was implemented in the configuration of the ESPER engine, which allows to handle it in an engine-level manner, instead of the (system) time-based processing by default.  Accuracy 15 Dog 1 Dog 2 Dog 3 Total detected events 12 20 152 Expected events 13 20 151 Accuracy 92.31% 100 % 100 %
  • 16. Results Analysis (2)  Latency In Level 3 events compared to the last previous event needed to match the pattern 16 Food Stealing Loud Vocalization Destructive Behavior Behavior ID 30 31 32 Max 1.160E-06 7.990E-06 1.273E-05 Average 1.160E-06 2.420E-06 2.080E-06 Min 1.160E-06 0.000E+00 0.000E+00 0.000E+00 2.000E-06 4.000E-06 6.000E-06 8.000E-06 1.000E-05 1.200E-05 1.400E-05 30 31 32 Max Average Min
  • 18. Conclusions and Future Work  Complex Event Processing can represent a significant contribution to the monitoring of dog’s behavior when left home alone, parting from basic behavior inputs, higher- level behavior events can be detected in order to produce only the adequate amount of notifications to the owner.  The application of CEP for the detection of behavior events can be interpreted as a kind of middleware application within a bigger IoT system. When integrated provides a full service for taking care of dogs home alone.  The behavior monitoring can be further extended to a broader range of higher-level pattern rules, including the prediction of unwanted behavior or diagnosis of separation anxiety problems. It also can be extended for other animals or more than one animal simultaneously. 18
  • 19. References 1. Konok V, Dóka A, Miklósi Á. The behavior of the domestic dog (Canis familiaris) during separation from and reunion with the owner: A questionnaire and an experimental study. Applied Animal Behaviour Science, 2011, 135(4): 300-308. 2. Frank D., Minero M., Cannas S., Palestrini C. Puppy behaviours when left home alone: A pilot study. Applied Animal Behaviour Science, 2007, 104, 61-701. 3. Lund J D, Jørgensen M C. Behaviour patterns and time course of activity in dogs with separation problems. Applied Animal Behaviour Science, 1999, 63(3): 219-236. 4. Lukham, David. 2002. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison Wesley. 5. Bhargavi.R, Vaidehi. Semantic intrusion detection with multisensor data fusion using complex event processing. Sadhana Indian Academy of Science, 2013, 38(2), 169-185. 6. Hänninen L1, Pastell M.CowLog: open-source software for coding behaviors from digital video. Behav Res Methods, 2009 May;41(2):472-6 7. Etzion, Opher and Niblett, Peter. 2011. Event Processing in Action. Manning. 8. Vaidehi V, Bhargavi R, Ganapathy K, et al. Multi-sensor based in-home health monitoring using complex event processing. Recent Trends in Information Technology (ICRTIT), 2011 International Conference on. IEEE, 2011: 570-575. 19
  • 20. References 9. Scaglia E, Cannas S, Minero M, et al. Video analysis of adult dogs when left home alone. Journal of Veterinary Behavior: Clinical Applications and Research, 2013, 8(6): 412-417. 10. Tae-ho Chung, Chul Park, Yong-man Kwon, and Seong-chan Yeon, “Prevalence of canine behavior problems related to dog-human relationship in South Korea – A pilot study”, Journal of Veterinary Behavior, 11 (2016) pp. 26-30. 11. Software Engineering Lab, Korea University, 2016. Pets Management System Based on IoT for Improvement of Living Conditions. 12. Moshnyaga V, Osamu T, Ryu T, Hashimoto K “Identification of Basic Behavioral Activities by Heterogeneous Sensors of In-Home Monitoring System”. 6th International Workshop, HBU 2015. 160- 174. 13. Martiskainen, Paula, et al. "Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines." Applied Animal Behaviour Science 119.1(2009):32-38. 14. Lianli Gao, Hamish A. Campbell, Owen R. Bidder, Jane Hunter. Corrigendum to “A web-based semantic tagging and activity recognition system for species' accelerometry data” [Ecol. Inf. 13 (2013) 47–56]. Ecological Informatics, Volume 22, July 2014, Page 81. 15. Esper Team and EsperTech Inc. “Esper reference”. Version 5.2.0 20