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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
/“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.
/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!
/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
/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”
/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
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
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
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
/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
/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
/Mining Minds System Deployment 12
/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
/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
/Mining Minds: User Weight Goal Setting 15
/Mining Minds: Weight Change Goal Setting 16
/Mining Minds: Recommendations Generation 17
/Mining Minds: Visualization 18
/App View: Goals & Recommendations 19
/App View: Reward Points 20
/ 21App View: Weight Management Progress
/Conventional and Mining Minds Core Platform Comparison 22
/
Feature exists (fully) Feature exists (partially) Feature does not exist
Mining Minds Core Platform vs Existing Solutions
/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
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

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Mining Minds: an innovative 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
  • 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

Editor's Notes

  1. 1
  2. 'Lifestyle' diseases linked to unhealthy habits kill millions of people prematurely
  3. To overcome the shortcomings of application-specific solutions and leverage the potential of health information systems in a wide sense, general frameworks capable of managing these resources are required.
  4. To overcome the shortcomings of application-specific solutions and leverage the potential of health information systems in a wide sense, general frameworks capable of managing these resources are required.
  5. Weekly plan Favorite activities list Monthly plan Recommendations based on fav. Activities 3 months plan Recommendations based on fav. Activities