1) The document discusses the development of third generation teleassistance systems to help address issues related to an aging population in Europe. 2) It proposes using intelligent monitoring techniques like activity recognition, sensor data analysis, and quality of life assessments to build more proactive, automatic, and integrated care systems. 3) The proposed solution involves building a system called eKauri that uses machine learning and ambient intelligence approaches to improve sensor reliability, recognize user activities, support remote caregivers, and assess clients' quality of life over time.

1. Third Generation Teleassistance
Intelligent Monitoring Makes the Difference
Xavier Rafael-Palou, Carme Zambrana, Stefan Dauwalder,
Enrique de la Vega, Eloisa Vargiu, and Felip Miralles

2. The Problem
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016

3. The Problem
Healthy Ageing
Elderly people aim to preserve their independence and
autonomy at their own home as long as possible
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
By 2020, around 25% of the EU population will be over 65
People aged from 65-80 will rise by nearly 40% between 2010-2030
From 2012, the over-60 population will increase by about 2 million people a year
The median age of the EU population increased from 35.2 years in 1990, to 40.9 years by 2010
IT IS URGENT TO PROMOTE HELTHY AGEING THROUGH ICT SOLUTIONS

4. The Problem
Towards Third Generation Teleassistance
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
1st generation
• Reactive
• No follow-up
• Not integrated into
the HIS

5. The Problem
Towards Third Generation Teleassistance
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
1st generation
HELP!
• Reactive
• No follow-up
• Not integrated into
the HIS
2nd generation
• Reactive
• Follow-up
• Partially integrated
into the HIS

6. The Problem
Towards Third Generation Teleassistance
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
3rd generation
• Proactive
• Totally automatic
• Smart follow-up
• Integrated into the
HIS
HEL!

7. AI Environment
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016

8. AI Environment
Techniques, Methods, Tools
Machine learning
Data mining
Ambient intelligence
Monitoring
Knowledge and reasoning
Planning and Scheduling
Robotics
Computer vision
Personalization and profiling
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
Coordination
Self-organization
Self-adaptation
Collaborative filtering
Sensor networks
Multi-agent systems
Decision support systems
Recommender systems

9. AI Environment
Techniques, Methods, Tools
Machine learning
Data mining
Ambient intelligence
Monitoring
Knowledge and reasoning
Planning and Scheduling
Robotics
Computer vision
Personalization and profiling
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
Coordination
Self-organization
Self-adaptation
Collaborative filtering
Sensor networks
Multi-agent systems
Decision support systems
Recommender systems

10. The Proposed Solution
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016

11. The Proposed Solution
Outline
Intelligent monitoring for…
 Improving Sensor Reliability
 Activity Recognition
 Supporting carers
 Assessing quality of life
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
eKauri: the teleassistance system

12. The Proposed Solution
Improving Sensor Reliability
High-level classifier
 SVM (RBF; γ = 1.0; C = 0.452)
Low-level classifier
 Novelty detection approach
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
The hierarchical approach
Rule-based Hierarchical Improvement
Accuracy 0.80 0.95 15%
Precision 0.68 0.94 26%
Recall 0.71 0.91 20%
F1 0.69 0.92 23%

13. The Proposed Solution
Sleeping Recognition
Supervised classifier: SVM (RBF; γ = 1.0; C = 1.0)
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
Sleeping recognition is aimed at reporting
 the time when the user went to sleep and woke up
 the number of sleeping activity
hours
 the number of rest hours

14. The Proposed Solution
Supporting carers
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
The role of carers is essential for remotely assisting people that
need assistance

15. The Proposed Solution
Supporting carers
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
The role of carers is essential for remotely assisting people that
need assistance

16. The Proposed Solution
Assessing Quality of Life
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
We defined a Visual Analogic
Scale QoL questionnaire
composed of the following items
 MOOD
 HEALTH
 MOBILITY
 SATISFACTION WITH CARE
 USUAL ACTIVITIES (SLEEPING)
 PAIN/DISCOMFORT
Item Accuracy
MOBILITY 0.76
SLEEPING 0.72
MOOD 0.81
Preliminary results

17. Conclusions
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016

18. Conclusions
Third Generation Teleassistance
August 30, 2016 AI: Context – Multimodal Analytics – Internet of Things
ECAI 2016
Time is ripe to adopt IoT in the dependency care sector
Intelligent monitoring makes the difference in providing feedback to
the users
User-centered solutions must be taken into account, involving final
users in each phase of the development
We need concrete solutions that give a twist in adapting innovative
strategies

19. eloisa.vargiu@eurecat.org