Ambient Intelligence refers to a digital environment that proactively supports people in their daily lives. It is an emerging discipline that brings intelligence to our living environments, makes those environments sensitive to us, and adapting according to the user’s needs. By enriching an environment with appropriate sensors and interconnected devices, the environment would be able to sense changes and support decisions that benefit the users of that environment. Such smart environments could help to reduce energy consumption and thus the cost of facilities, improve safety and security, while at the same time increase user’s comfort. One specific area of interest is the application of ambient intelligence in Ambient Assisted Living, where the home environment provides assistance with daily living activities for people with different cognitive and physical disabilities. For example, technologies are available to help older adults to live longer and more independently in their own homes. To enhance the intelligence of the environment, Computational Intelligence techniques as a set of nature-inspired computational methodologies are available to address such complex problems for which traditional approaches are ineffective. This lecture will provide a review of the technologies and environments that comprise Ambient Intelligence, as well as how changes in the environment are reflected in the overall design of an adaptive ambient intelligence environment.

1. From Non-Intelligent to Intelligent
Environments: a Computational and
Ambient Intelligence Approach
Inaugural Lecture by
AHMAD LOTFI
School of Science and Technology
Nottingham Trent University

2. Spot the Difference!
Non-Intelligent Intelligent
2

3. Spot the Difference #2
House BHouse A
3

4. Thermogram
Spot the Difference #2
Less Intelligent More Intelligent

5. What is Intelligence?
•The capacity to learn and solve
problems, in particular:
• the ability to solve novel problems
•the ability to act rationally
•the ability to act like humans
5

6. Test of Intelligence
H0W 0UR M1ND5 C4N D0 4M4Z1NG 7H1NG5!
1MPR3551V3 7H1NG5! 1N 7H3 B3G1NN1NG 17
WA5 H4RD BU7 N0W, 0N 7H15 LIN3 Y0UR M1ND
1S R34D1NG 17 4U70M471C4LLY W17H0U7 3V3N
7H1NK1NG 4B0U7 17.
B3 PROUD! 0NLY C3R741N P30PL3 C4N R3AD
7H15. C4N YOUR COMP73R R34D 4ND
UND3R574ND 7H15?
6

7. Edie and Edd
7

8. Intelligent Door ?

9. Story of the Door
Mechanical Intelligence
Computational Intelligence
1 2
3 4 5
Artificial Intelligence
Machine Intelligence
9

10. Machine Intelligence
• Artificial Intelligence (AI) - The study of
computer systems that attempt to model
and apply the intelligence of the human
mind.
• Computational Intelligence (CI) - Use of
soft computing techniques to mimic the
ability of human mind in effectively
employing modes of reasoning that are
approximate rather than exact.
10

11. Machine Intelligence
Autonomy (self-diagnostics,
fault tolerance, self-tuning)
Man-Machine Integration
(Human-like
understanding/communication,
emergence of emotion)
Bio-inspired Behaviour
(Cognitive-based, biologically
motivated behaviour)
Controllability (Adaptation,
thinking and planning)
System 1
System 2
11

12. Spot the Difference #3
Less Intelligent
(IQ ≈ 100)
More Intelligent
(IQ ≈ 190)
GeorgeWBush
GarryKasparov
12

13. IQ
Intelligence Quotient (IQ) is a
composite indicator meant to
measure people cognitive abilities
in relation to their age group.
13

14. MIQ
Machine Intelligence Quotient
(MIQ) is a comparative indicator of
machine human-like intelligence.
14

15. TV’s MIQ
15

16. MIQ
16

17. Intelligent Environment
Paradigms
1.Reactive System
2.Deliberative System
17

18. 1. Reactive System
Sense Act
Smoke Alarm
PIR
Thermostat
Light
Heating/Cooling
Alarm
18

19. Light
Heating/Cooling
Alarm
2. Deliberative System
Think
ActSense
NEST – Learning
Thermostat
Thermostatic
Radiator
Valve
19

20. Room
Temperature
Door Entry
Light
Sofa
Occupancy
WindowsRoom
Occupancy
Door Lock
Smoke
Detector
Intelligent
Environments
20

21. Intelligent
Offices
Light Control
Room Temperature
PC/Laptop Control
Office Occupancy
21

22. Definition
• Intelligent Environments are spaces with
embedded systems and information and
communication technologies creating
interactive spaces that bring computation
into the physical world.
• Ambient Intelligence refers to a digital
environment that proactively supports
people in their daily lives.
22

23. … or ...IntelligentEnvironments
Ambient
Intelligence
Ambient Assisted Living
Ubiquitous
Computing
Pervasive
Computing
Context Aware
Internet of Things
…
Intelligent Offices
Intelligent Cars
Intelligent Cities
…
23

24. Ambient Assisted Living

25. Definition
• Ambient Assisted Living (AAL) is the
use of information and communication
technologies in a person's daily living
and working environment to enable
individuals to stay active longer and
live independently into old age.
• This could be as simple as an alarm to
remind a person to take medication or
as sophisticates as a mobility scooter
or electric wheelchair to help with
daily shopping.
25

27. Sources: CORDIS, TSO “Carers of Elderly People – Summary of the Background Evidence” Alzheimer’s Society
What is the Problem?
• By 2050, people aged 65-79 are expected to make up almost 1/3 of the
population in Europe
• Over that period, the population of very elderly (80+) will rise by 180%
• One in 50 people aged 65-70 have a form of dementia, rising to one in
five over 80
• Currently there are 7 million carers of the elderly in the UK alone.
27

28. Tools & Infrastructure
What makes Ambient Assisted Living (AAL) possible?
A. Assistive & Social Robotics
• … a robot that interacts and communicates with humans or
other devices …
B. Wearable & Mobile Devices
C. Ambient Assisted Living Homes
• ... digital environment that proactively supports people in
their daily lives
28

29. A. ASSISTIVE & SOCIAL ROBOTICS
Tools & Infrastructure

30. Social Robots
• Semi-autonomous robots
(mobile platforms, humanoid,
…)
• Interacting with people in their
own space
• Augmenting healthcare givers
• Providing rehabilitation and
lifestyle support
30

31. Social Robots (Examples)
VGo Anybots QB DoubleRobot MantaroBot 31

32. Assistive Robots (Examples)
User Assisted
Mobility Scooter
Assisted RoboticsActive Walker
http://iwalkactive.com
32

33. B. WEARABLE & MOBILE DEVICES
Tools & Infrastructure

34. • Applications
• Health monitoring
• Navigation and stray prevention
• Mobile persuasive technologies
Wearable & Mobile Sensors
iHealth - Wireless
Activity and Sleep
Tracker
GPS Smart Shoe Smart CanePanic Alarm
34

35. C. AMBIENT ASSISTED LIVING HOMES
Tools & Infrastructure

36. Unobtrusive “Activities of Daily
Living” Monitoring System
• To gather data on the routine activities of elders e.g.,
getting out of bed, going to the bathroom, preparing
meals, taking medications etc. without altering the
elders' normal behaviour
• Preferably wireless sensors should be used, along with
a small computerised receiver to collect data that are
then analysed and posted to a secure central web site
for viewing by the carer/relative
• The adult children of frail elders living alone and at a
distance can be sent reports or alerts daily/weekly in
the form of e-mail or phone calls.
36

37. Ambient Assisted Living
Homes
Activities
of Daily
Living
(ADL)
Kettle
Microwave
Fridge
Doors
Food cupboards
Cutlery drawer
Bed
Room occupancy
37

38. Telecare Solutions
38

39. Telecare Solutions
39

40. Sensors …
• Fall Detector
• Bed Occupancy Sensor
• Chair Occupancy Sensor
• Carbon Monoxide Alarm
• Door Usage Sensor
• Electrical Usage Sensor
• Enuresis Sensor
• Flood Detector
• Gas Shut Off Valve Solution
• Natural Gas Detector
• Heat Detector
• Medication Dispenser
• Passive Infra-Red Movement Detectors
• Pressure Mat
• Pull Cord
• Smoke Detector
• Temperature Extremes Sensor
40

41. Ambient Assisted Living Homes -
the possibilities
41

42. Activities of Daily Living
(Example)
http://www.tunstall-life.com/ 42

43. Occupancy Signal – Single
Occupancy
Time
LoungeSensor
Time
KitchenSensor
0
1
• No parallel activity of PIRs
• No uncertainty on where the occupant is.
43

44. Start-time and Duration
Bedroom Kitchen
Lounge Corridor
44

45. Start-time and Duration for
Bedroom Signal
45

46. Multiple Occupancy Scenario
No parallel activities
Parallel activities
46

47. Hour
Behaviour Pattern
Room transition
Pattern Recognition
Hour
47

48. Telehealth Solutions
Remote exchange of data
between a patient at
home and their
clinician(s) to assist in
diagnosis and monitoring
typically used to support
patients with Long Term
Conditions.
48

49. Telehealth Solutions
49

50. ACTIVITIES CLASSIFICATION
AND ABNORMALITY DETECTION
Research Area

51. Fuzzy Classification
51

52. User Activities Outlier
Detection
• To detect any deviation in the day-to-day
behavioural patterns of occupants using data
generated from low level sensors.
• To develop a good understanding of the normal
behaviour and distinguish any abnormalities and
possible trend in the behavioural changes.
• To examine the application of distance measures
and Fuzzy rule-based system in identifying the
abnormality within the behavioural patterns of
an occupant.
52

53. Case Studies
• Case 1
• This environment is monitored using JustChecking Monitoring system.
• The data is collected for 14 months.
• It is based on a single elderly occupant
• Front and back door sensors, lounge, kitchen, bedroom, bathroom and
upstairs motion sensors are used.
• Case 2
• An elderly person was first prescribed some medications
• After a few days, her medications were replaced
• This environment is monitored using JustChecking Monitoring system.
• Case 3
• The elderly person uses a walker support to help her in moving around her
apartment.
• Four motion sensors are used: lounge, kitchen, bedroom and corridor
sensors. Two door entries are used: bathroom and the main entrance.
• The data is collected for a couple of weeks where holidays and weekends are
not included.
53

54. Extremely Outlier NormalMore or Less Normal
Scattered plot for the 1st and 2nd principal components of the back door entry sensor data used in
case study I with classification.
Results on Case Study (1)
54

55. Extremely Outlier NormalMore or Less NormalSlightly Outlier
Results on Case Study (1)
Scattered plot for the 1st and 2nd principal components of the lounge motion sensor data used in
case study I with classification.
55

56. Slightly OutlierNormal More or Less Normal
Results on Case Study (2)
Scattered plot for the 1st and 2nd principal components of the bedroom motion
sensor data used in case study II with classification.
56

57. Extremely OutlierNormal
Results on Case Study (3)
Scattered plot for the 1st and 2nd principal components of the front door entry sensor data
used in case study III with classification.
57

58. PREDICTIVE AMBIENT INTELLIGENT
ENVIRONMENT
Research Area

59. Behaviour Modelling
Techniques
• Hidden Markov Model (HMM) - is a statistical
model in which the occupant behaviour is
assumed to be a Markov process.
• Recurrent Neural Network (RNN) - The neural
network based approaches use large time series
data sets to learn the relationship between the
input data and output data.
• The accuracy of both statistical and neural network
based methods degrade rapidly with increasing
prediction lead time.
59

60. 6 Hours Ahead Prediction
Using ESN
Predicted values for bedroom
occupancy sensor
Predicted values for corridor
occupancy sensor
60

61. Research Projects …
• School Transport Automatic Register
• Energy Efficiency in Social Housing
• Activities Recognition and Worker Profiling in
the Intelligent Office Environment
• Intelligent Care Guidance and Learning
Services Platform for Informal Carers of the
Elderly (iCarer)

62. Problem Definition
• We want the office environment to provide better
worker comfort combined with reduced energy use
• Setting the correct heating level
• Setting satisfactory lighting
• Only turning off computers when required and vice versa
• We need to be able to identify the patterns of
individuals’ Activities of Daily Work (ADW), so as to tailor
their work environment to their particular needs.
• Thus we have to investigate the similarities in
behavioural patterns of different users in an office
environment.

63. System Architecture
• Intelligent office monitoring
used 11 non-intrusive
sensors.
• Door and Window entry
sensors
• PIR
• Temperature sensor
• Humidity sensor
• Chair pressure pad
(vibration sensor)
• Mouse and Keyboard
activities
System architecture
for intelligent office environment
System Architecture and Data
Collection System

64. Can We See a Pattern?
• Aggregated data can appear complex

65. Fuzzy State Machine

66. Are the States ‘Sensible’?
• The office users were asked to generate a diary, and the contents
compared with the basic states deduced from the sensors
• This is promising, since it means that an Intelligent Office is unlikely
to misinterpret states and annoy the office worker
Tuesday, worker #2,
diary of activities
Tuesday, worker #2,
sensor based states

67. Experiments / Results
• Average/Moving Average are
used to define the duration
membership functions.
• The worker clearly has
different patterns on different
days of the week
• The coarse profile, considers
only the average in order to
provide the first stage of
modelling
• There will be some seasonal
changes so that the pattern of
work may very during the
summer and winter time
Sample of chair occupancy duration
over three weeks.

68. Fuzzy Characteristics Matrix
Chair Activities on Wednesday Chair Activities on Friday
• Size of the circle represents the likelihood of the activity
occurrence for the specified start time and duration for
that day of the week.

69. Fuzzy Characteristics Matrix
Chair Activities on Wednesday Chair Activities on Friday
• Less important activity occurrence (small circles) could
be discarded.

70. Research Projects …
• School Transport Automatic Register
• Energy Efficiency in Social Housing
• Activities Recognition and Worker Profiling in
the Intelligent Office Environment
• Intelligent Care Guidance and Learning
Services Platform for Informal Carers of the
Elderly (iCarer)

71. Intelligent Care
• The informal carers are becoming crucial agents in the
elderly’s care and support.
• Carers suffer distress or over-work episodes appeared due
to lack of knowledge about older adult’s care.
• Support and distress relief in caregivers should be a key
issue in the home-care process of these older adults.
• The project aimed at developing a personalized and
adaptive platform to offer informal carers support by means
of monitoring their activities of daily care and psychological
state, as well as providing an orientation to help them
improve the care provided.
http://icarer-project.eu/ 71

72. iCarer Platform
72

73. Concluding Remarks
• Ambient Intelligence is foreseen to
be present everywhere in the
future world and to ease human
living.
• To build an environment which will
be natural, informative and caring
from human perspective.
73

74. Machine Intelligence Evolution
74

75. Acknowledgements
75

76. T H A N K S