+ All Categories
Home > Technology > Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Date post: 20-Oct-2014
Category:
View: 294 times
Download: 1 times
Share this document with a friend
Description:
Action COST TU1204 - WG2 meeting, Madrid, 2013-12-16
Popular Tags:
21
Trans-disciplinary Knowledge Platform: Sensors, Biometrics and Big Data (information, explicit and tacit knowledge, good/best practice) Analytics for Services Prof. PhD. DrSc. A.Kaklauskas, Chairman of the Department of Construction Economics and Property Management, Director of the Institute of Internet and Intelligent Technologies, Vilnius Gediminas Technical University, Vilnius, Lithuania
Transcript
Page 1: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Trans-disciplinary Knowledge Platform: Sensors, Biometrics and Big Data

(information, explicit and tacit knowledge, good/best practice)

Analytics for Services

Prof. PhD. DrSc. A.Kaklauskas, Chairman of the Department of Construction Economics and Property Management, Director of the Institute of Internet and Intelligent Technologies, Vilnius Gediminas Technical University, Vilnius, Lithuania

Page 2: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Data Rich World includes (see ANNEX 2 - Chair Presentation): - SENSORS (Smart Meters, Smart Phones/Tablets, GPS Devices/CCTV, Weather Sensors, Building Sensors). - ANALYTICS (Algorithims, Closed-Loop Systems, Optimize Consumption, Location Sensing). - SERVICES (Location Services, Crowd Sensing, Traffic Management, Smart Electric Grid, Disaster Management, Security).

Page 3: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Internet of Things in the context of Smart Environments and Applications (IERC)

Page 4: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Key Trends to Watch in Gartner 2012 Emerging Technologies Hype Cycle

Page 5: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Hospital Real-time Sensor Big Data and Staff Decision Support System: •  Hospitals actively monitor patients, their location and behaviour using

sensors. •  System integrate third-party information sets from sensors, health

information coming from patient records, test results, reports, best practice, journals, doctors, gym, assets, networks, personal sensor systems in real time.

•  Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses. Additionally, sophisticated clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care.

•  Handheld devices provide staff with patients status, scheduled treatments, activities, and sensor diagnostics.

•  The System was also intended to support Emergency Services, Police, Manufacturing, Autonomous Systems and of course Health.

Page 6: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Augmented Reality Housing Health and Safety System (VGTU, Lithuania) For inhabitants: http://iti.vgtu.lt/VGTU_Lomonosov/ Recommender Subsystem: http://iti.vgtu.lt/imitacijosmain/simpletable.aspx?sistemid=517 http://iti.vgtu.lt/sveikasbustas/kapateikti.aspx Virtual tour: http://iti.vgtu.lt/imitacijosmain/oana_fatima1.swf

Page 7: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Athenian democracy (around 550 BC)

Page 8: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Collaborative e-democracy intelligent platform Oficials (public administrations, politicians, ministers, parliamentarians, etc.) and citizens (businessmen, media, local communities, individual citizens, etc.) can collaborate on the development of public policies (courses of action, regulatory measures, laws, and funding priorities).The inhabitants would hold their last voting possibility on the most important public policy issues. Citizens became both co-creators and users of city services and infrastructure.

Page 9: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Internet of Things embedded in Internet of Energy applications (IERC)

Page 10: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Intelligent Library and Passive House Intelligent System (VGTU, Lithuania)

http://iti.vgtu.lt/ieeppt/

Page 11: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Body language and biometrics technologies

Page 12: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data
Page 13: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Multimodality

Page 14: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Pupil and pupil blinks data base (VGTU, Lithuania)

Page 15: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Pulse rate data bases (VGTU, Lithuania)

Page 16: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

The interdependence linking physiological parameters of students to their learning productivity and the interest in

learning (VGTU, Lithuania)

Page 17: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Correlation of relationships between pupil (iris)

diameter and stress level

5

7

9

11

13

15

17

19

34 122110 11 11

6 49 127 18 22 51 89 14 5 94 42 41 31 55 17 10

klausimai

bala

i

1,75

1,76

1,77

1,78

1,79

1,80

1,81

1,82

1,83

1,84

vyzdži

o di

amet

ras

Page 18: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data
Page 19: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Biometric computer mouse (VGTU, Lithuania): - temperature, - humidity, - skin conductance, - intensity of pressing, - heart rate. Mouse Events Capture, Collection and Feaure Extraction Subsystem: - speed of mouse pointer’s movement, - acceleration of mouse pointer’s movement, - amplitude of hand tremble, - scroll wheel use, - right- and left-click frequency, - idle time. Biometrcic Finger: - humidity, - electrogalvanic skin conductance, - skin temperature, - heart rate. Blood pressure and heart rate Subsystem: - heart rate, - systolic blood pressure, - diastolic blood pressure.

Page 20: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

The Recommender System and motivational, educational persistence and social learning theories

and the database of best global practices

The Recommender System will use motivational, educational persistence and social learning theories and

the database of best global practices based on above theories to come up with recommendations for students on

how to improve their learning efficiency.

Page 21: Arturas Kaklauskas - Trans-disciplinary knowledge platform - sensors, biometrics and big data

Best learning productivity enhancement recommendations compiled per Maslow‘s Hierarchy of

Needs


Recommended