Semantic and Fuzzy Modelling for Human Behaviour Recognition in Smart Spaces. A case study on Ambient Assisted Living.
24.4.2015 Åbo Akademi University, Finland
1. Semantic and Fuzzy Modelling for Human Behaviour Recognition in Smart
Spaces. A case study on Ambient Assisted Living
1Doctoral Defense 24th April 2015,Turku, Finland
Natalia A. Díaz Rodríguez
Supervisors: Prof. Johan Lilius and Prof. Miguel Delgado Calvo-Flores. Advisor: Prof. Manuel Pegalajar Cuéllar
Turku Centre for Computer Science (TUCS), Dept. of Information Technologies, Åbo Akademi University (Finland)
Dept. of Computer Science and Artificial Intelligence, University of Granada (Spain)
2. 2
SPAIN: 15m of elders in 2049 (1/3 of the population) (INE)
FINLAND population 65+ years: 18.14% [1]
• [1] http://www.finnbay.com/media/news/government-prepares-to-set-out-new-requirements-for-senior-caretakers/
5. Ambient Assisted Living (AAL): usage of
technology to provide assistance to people who
needs it in their daily activities, in the less
obstrusive way
Aim: Independent living, safety, support
older/disadvantaged people
Includes: methods, systems, products and
services
5
Background
6. Activity Recognition in Smart Spaces
6
[Image: http://www.businesskorea.co.kr/sites/default/files/field/image/smart%20home.jpg + The noun project]
8. Handling uncertainty, vagueness
and imprecision
Broken/ missing sensors
Incomplete data, vagueness
etc.
Different ways of perfor-
ming activities
– Different object usage
Behaviour change
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10. Methods
WHY Semantic Technologies & Ontologies?
Semantic Web: well-defined meaning
Ontology:
– In Philosophy: study of entities and their
relations
– In Artificial Intelligence: “Explicit specification of
a conceptualization” [Gruber, 93]
– Web Ontology Language (OWL)
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14. Methods: Fuzzy Logic
WHY fuzzy (description) logics and fuzzy
ontologies?
Real life is not black & white
– Classical (Crisp) Logic: True/False
– Fuzzy Logic: [0, 1]
• e.g. blond, tall
For automatic reasoning about uncertain,
vague or imprecise knowledge
For natural language expressions
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15. 15
Case study on Ambient Assisted Living:
A fuzzy ontology for activity modelling and recognition
[Image: http://www.harmonizedsystems.co.uk/]
Example: Take Medication
16. 16
Case study on Ambient Assisted
Living: A fuzzy ontology for activity modelling and
recognition
Classes, Individuals, Data Properties and Object Properties
SUBJECT PREDICATE OBJECT
User performs activity Taking medicine =
(0.3 User performs sub-activity reach Cup or Medicine Box)
(0.3 User performs sub-activity move Cup or Medicine Box)
(0.1 User performs sub-activity place Cup or Medicine Box)
(0.1 User performs sub-activity open Medicine Box)
(0.1 User performs sub-activity eat Medicine Box)
(0.1 User performs sub-activity drink Cup)
23. A visual language to
configure the Smart Space behaviour
TARGET USER: non-technical
background
AIM:
– Rapid & easy programming of applications/
services
– Improve interoperability and usability
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25. Main contributions
1. A set of ontologies to model human behaviour and tackle
uncertainty and vagueness inherent to real life
2. An architecture that integrates Semantic Web and
Fuzzy Logic for interpretable activity recognition
3. A hybrid knowledge-based and data-driven algorithm for
real-time, effective and robust activity recognition (84.1%
precision)
4. Design and development of a toolbox for non-expert
users and rapid and easy programming of Smart Spaces
[4 Journals -3 on 3rd Q1-, 9 conference papers, Google Anita Borg,
Nokia and HLF scholar. Entrepreneurship award]
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28. Thank you for your attention
Natalia A. Díaz Rodríguez
https://research.it.abo.fi/personnel/ndiaz
ndiaz@abo.fi
Embedded Systems Lab. Dept. of Information Technologies
Åbo Akademi University, Turku, Finland
TUCS (Turku Centre for Computer Science)
Dept. of Computer Science
and Artificial Intelligence
University of Granada, Spain
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29. Appendix
DTW: O(mn) (m,n length of the time
series)
PAA compression size = 2
Take out food is a subsequence of
microwaving (Subsumption heuristic/filter)
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31. 31
Modelling activities with
fuzzy ontologies
Classes, Individuals, Data Properties and Object Properties
SUBJECT PREDICATE OBJECT
Filomena is a Person
Filomena has heart rate 60
Filomena performs sub-activity Reach glass
Filomena performs sub-activity Move medicine
Filomena performs sub-activity Pour water in glass
Filomena performs sub-activity Eat medicine
Filomena performs sub-activity Drink from glass
32. A crucial but challenging task in Ambient
Intelligence and AAL. Requires:
Context-awareness and heterogeneous data
sources
Training data: examples
Common-sense knowledge
Adaptation of behaviours
Alzheimer, Parkinson
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Human Activity Recognition