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

Pers

onal

ized

Hea

lthca

re

Man

agem

ent S

ervi

ces

Personal Big Data

Variety

Velocity

Volume

Evolutionary KnowledgeKnowledge

Feedback

User Adoption and EngagementUI/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 PlatformsProposed 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 Inter-

mediate Data Generation

Privacy

Personalized data protection technology• Service Aware Autonomous

anonymization technology• Oblivious Term Matching• Private Matching

Hong gil dong, KHU180cm, age 25

->Hong**, **Univ170-180cm, age 20-30

Oblivious Term Matching

Hong gil dong, KHUKim chul su, KHU

->86e0109, 638560c691ed13, 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 SystemsExercise, activity, etc.

Structured static knowledge

Mining Minds Aims: Evolutionary Knowledge

Feedback

Knowledge maintenance engine

Update knowledge Userrequirements

Knowledge Maintenance

Knowledgebase update technique based on user feedback • Expert and automatic knowledge

maintenance• Multi-level maintenance

SelectorAutomatic 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

/ 9Existing UI/UX Technology

Create UI/UXRule

UI/UX Knowledge

Gender, age, Using pattern… etc

Structured static knowledge

ProvideUI

Provide Feedback UI

UI/UX Authoring tool

Gender, age, using pattern, feedback, etc

Personalized dynamic knowl-edgeAdaptive UI/UX

Context based personalized and customized UI• Adaptive UI• UX

Survey individual UX

BehaviorMeasurement

User-machine interaction analysis based on UX• Feedback• Behavior Measurement

Trust: App Usage LessInteraction: Less No of Clicks

Reaction: ComplexityFunctionality: Less features

Predictability: Easy NavigationIndividuality: Color Scheme

Induce habituation

Mining Minds Aims: User Adoption and Engagement

Proposed UI/UX Technology

10/Mining Minds Architecture

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

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/Mining Minds System Deployment 12

/Mining Minds Technologies 13

/Mining Minds Inter-Layer Communication 14

15/Mining Minds: User Weight Goal Setting

16/Mining Minds: Weight Change Goal Setting

17/Mining Minds: Recommendations Generation

/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 BañosUbiquitous Computing Lab (UCLab)Kyung Hee University (KHU), South

KoreaEmail: oresti@oslab.khu.ac.kr

Web: http://uclab.khu.ac.kr/oresti