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Mining Minds: an innovative framework for personalized health and wellness support


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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.

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Mining Minds: an innovative framework for personalized health and wellness support

  1. 1. Dr. Oresti Banos Ubiquitous Computing Lab (UCLab) Kyung Hee University, South Korea 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. 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. 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. 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. 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. 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. / 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. / 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. / 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. 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. 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. 12. /Mining Minds System Deployment 12
  13. 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. 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. 15. /Mining Minds: User Weight Goal Setting 15
  16. 16. /Mining Minds: Weight Change Goal Setting 16
  17. 17. /Mining Minds: Recommendations Generation 17
  18. 18. /Mining Minds: Visualization 18
  19. 19. /App View: Goals & Recommendations 19
  20. 20. /App View: Reward Points 20
  21. 21. / 21App View: Weight Management Progress
  22. 22. /Conventional and Mining Minds Core Platform Comparison 22
  23. 23. / Feature exists (fully) Feature exists (partially) Feature does not exist Mining Minds Core Platform vs Existing Solutions
  24. 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. 25. Thank you for your attention. Questions? 25 Dr. Oresti Baños Ubiquitous Computing Lab (UCLab) Kyung Hee University (KHU), South Korea Email: Web: