The world is witnessing a spectacular shift in the delivery of health and wellness care. The key ingredient of this transformation consists in the use of revolutionary digital technologies to empower people in their self-management as well as to enhance traditional care procedures. While substantial domain-specific contributions have been provided to that end in the recent years, there is a clear lack of platforms that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these technologies. This work presents Mining Minds, an innovative framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized healthcare and wellness support. Mining Minds embraces some of the currently most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, and state-of-the-art concepts and methods, such as Context-Awareness, Knowledge Bases or Analytics, among others. This paper aims at thoroughly describing the efficient and rational combination and interoperation of these modern technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support.

1. Dr. Oresti Banos
Ubiquitous Computing Lab (UCLab)
Kyung Hee University, South Korea
oresti@oslab.khu.ac.kr
http://uclab.khu.ac.kr/oresti
9th International Conference on
Pervasive Computing Technologies for
Healthcare (Pervasive Health 2015)
Istanbul, Turkey
Mining Minds: an innovative
framework for personalized
health and wellness support

2. /“The Slow-Moving Public Health Disaster”
Diseases linked to lifestyle choices are currently
the biggest cause of death worldwide:
• Cardiovascular conditions, cancers, chronic respiratory
disorders, obesity and diabetes, represent more than 60% of
global deceases, half of which are of premature nature
• Most of these diseases are fairly associated to common risk
factors, namely, tobacco and alcohol use, unwholesome diet
and physical inactivity
• This "lifestyle disease" epidemic causes a much greater
public health threat than any other epidemic known to man
• Millions of lives could be saved if the world over the next
decade invests $1-3 per person on promoting healthier
habits
2
Global targets for prevention and control of
“lifestyle diseases” to be attained by 2025
Source: WHO, “Global status report on noncommunicable diseases 2014,” World Health Organization, Tech. Rep., 2014.

3. /Digital Health Revolution
• ICT are called upon to be a cornerstone of the new health era,
playing a crucial role in empowering people to take charge of
their own health and wellness, by providing them timely and
ubiquitously with personalized information, support and
control
• Many applications and devices are increasingly available;
however, these systems are not currently meeting the needs
of those they serve, and there is a paucity of current offers
adding value
• The immediate targets of these solutions are healthy lifestyle
services, especially oriented to the fitness domain, which
primarily allow to track primitive user routines and provide
simple motivational instructions
3
Need of
Digital
Health and
Wellness
Frameworks!

4. /Key Limitations of Existing Digital Health Frameworks
• Most mobile health frameworks are bound to the
computational capabilities of the smartphone, require
continuous maintenance and updates of end-user
applications and normally trap data into their devices
• Moreover, multiple systems and applications can be
generate similar health data and outcomes leading to
unnecessary redundancy and overcomputation
• These systems mostly operate on-demand, thus
determinants of health and wellness states can be
also lost if not registered in a continuous manner
• Platforms devised to share and integrate health and
wellness data underuse cloud resources, by only
utilizing them for storage
4

5. /Mining Minds in a Nutshell 5
“Collection of innovative services, tools, and techniques, working collaboratively
to investigate on human's daily-life routines data generated from heterogeneous
resources, for personalized wellbeing and healthcare support”

6. /Mining Minds Scope 6
PersonalizedHealthcare
ManagementServices
Personal Big Data
Variety
Velocity
Volume
Evolutionary Knowledge
Knowledge
Feedback
User Adoption and Engagement
UI/UX
Education
Goal Objectives Challenges

7. / 7
Smart Cup
Smartphone
Survey Data
Social Networks
Wearable Sensor
Kinect Camera
Personal
big data
Volume
• 800 thousand personal
data
• 5 billion SNS data
Analysis &
Processing
Existing Big Data Platforms
Proposed Big Data Platform
Multimodal Sensor
Variety
Velocity
Heterogeneous sensory data and
structured and unstructured diverse
big data processing
• Conformed data structure
• Data Representation & Mapping
Real time data processing technology
which requires timely analysis
• Real-Time Data Labeling
• Streaming Data Retrieval and
Intermediate Data Generation
Privacy
Personalized data protection
technology
• Service Aware Autonomous
anonymization technology
• Oblivious Term Matching
• Private Matching
Hong gil dong, KHU
180cm, age 25
->Hong**, **Univ
170-180cm, age 20-30
Oblivious Term Matching
Hong gil dong, KHU
Kim chul su, KHU
->86e0109, 638560c
691ed13, 152aa3a
Private Matching
Real-Time Sensor Data:
1.2, 1.0, 2.2, 3.1
->1.2, 1.0, 2.2, 3.1, “Work”
Real-Time Data Labeling
“Work“, “Seould Gangnam”,
“16C”, “165kcal”
-> “Work”, “165kcal”
Streaming Data storing
(Storing automatic data selection)
Mining Minds Aims: Personal Big Data

8. / 8
Generate structured
knowledge
Knowledge Base
Provide
recommendation
service
Existing Knowledge Maintenance Systems
Exercise, activity, etc.
Structured static knowledge
Mining Minds Aims: Evolutionary Knowledge
Feedback
Knowledge maintenance engine
Update knowledge User
requirements
Knowledge Maintenance
Knowledgebase update technique
based on user feedback
• Expert and automatic knowledge
maintenance
• Multi-level maintenance
Selector
Automatic Algorithm selection using
Meta-learning
• Meta-features computation
• Algo. performance evaluation
• Problem meta-features to Algo.
performance Mapping
Classification Algorithms
-> J48, SVM, NB, ...
Knowledge Management
-> Data Curation,
Information Curation,
Service Curation
Personalized dynamic knowledge
Proposed
Knowledge
Maintenance
System

9. / 9
Existing UI/UX Technology
Create UI/UX
Rule
UI/UX Knowledge
Gender, age,
Using pattern… etc
Structured static knowledge
Provide
UI
Provide
Feedback UI
UI/UX Authoring tool
Gender, age, using pattern,
feedback, etc
Personalized dynamic
knowledgeAdaptive UI/UX
Context based personalized an
d customized UI
• Adaptive UI
• UX
Survey individual UX
Behavior
Measurement
User-machine interaction
analysis based on UX
• Feedback
• Behavior Measurement
Trust: App Usage Less
Interaction: Less No of Clicks
Reaction: Complexity
Functionality: Less features
Predictability: Easy Navigation
Individuality: Color Scheme
Induce habituation
Mining Minds Aims: User Adoption and Engagement
Proposed
UI/UX
Technology

10. /Mining Minds Architecture 10
Delivers timely and accurate personalized
cross-domain recommendation based on
domain knowledge and users
preferences/context
Creates and maintains health and wellness
knowledge using expert-driven and data-
driven approaches
Provides real-time data acquisition from
multimodal data sources and its
persistence using big data technologies.
Activity and context data are mapped for
life-logging and personalized predictions
from life-log ontology
Facilitates information to the
users in the most intuitive
manner, in a secure environment
reflecting their personal needs
and preferences
Converts the data obtained from the user
interaction with the real and cyberworld,
into abstract concepts or categories, such
as physical activities, emotional states,
locations and social patterns, which are
intelligently combined to determine and
track context and behavior

11. /Mining Minds Scenario
• Personalized Recommendations
• Preferences, Activity Level and
Possessions
• MM Platform development
• Services based on layered
architecture
• Personalized Big Data
Processing
• Considers multiple users
• Users Feedback
• For knowledge evolution
11

12. /Mining Minds System Deployment 12

13. /Mining Minds Technologies 13
Smartphone Platform
Java
(JDK 1.8)
Java Runtime Environment (JRE 1.8)
Java
WS
SOAP
Communication PlatformIntegrated Development Environment Programming Language
Hosting Server
Database
Hypervisor
Guest Operating System

14. /Mining Minds Inter-Layer Communication 14
Gateway
Client Application
Data Comm.
Sensor Data
Accumlator
UI/ UX
Dashboard
Sensor Data
Write/Send
kSOAP Serializer
Information
Input Adapter
Parsing
Output Adapter
Attach Metadata
activity labels
parsed data
metadata
Smartphone-based
Activity Recognizer
Preprocessing
Segmentation
Feature Extraction
Activity
Recognition &
GPS Validation
Location Detector
Detect Location
location
DataCuration
WebserviceStub

15. /Mining Minds: User Weight Goal Setting 15

16. /Mining Minds: Weight Change Goal Setting 16

17. /Mining Minds: Recommendations Generation 17

18. /Mining Minds: Visualization 18

19. /App View: Goals & Recommendations 19

20. /App View: Reward Points 20

21. / 21App View: Weight Management Progress

22. /Conventional and Mining Minds Core Platform Comparison 22

23. /
Feature exists (fully) Feature exists (partially) Feature does not exist
Mining Minds Core Platform vs Existing Solutions

24. /Conclusions 24
• Lifestyle diseases linked to unhealthy habits kill millions of people prematurely
• Digital health solutions are increasingly available; however, application-specific
systems present important limitations to widely inspect on human’s lifestyles
• Mining Minds, a novel digital framework, is presented to seamlessly investigate
on people’s behavior and lifestyles in an holistic manner, through mining
human’s daily living data generated through heterogeneous resources
• An initial realization of the key architectural components, as well as an
exemplary application that showcases some of the benefits provided by Mining
Minds, have also been presented.
• Next steps include to complete the implementation of the devised architecture
as well as to evaluate its services on a large scale testbed

25. Thank you
for your
attention.
Questions?
25
Dr. Oresti Baños
Ubiquitous Computing Lab (UCLab)
Kyung Hee University (KHU), South Korea
Email: oresti@oslab.khu.ac.kr
Web: http://uclab.khu.ac.kr/oresti