“Mining Minds” is a novel framework aimed at comprehensively mining human’s daily life data generated from heterogeneous resources for producing personalized health and wellness support. Mining Minds philosophy revolves around the concepts of data, information, knowledge and service curation, which refer to the discovery, processing, adaptation and evolution of both contents and mechanisms for the provision of high quality support services. Motivated by these concepts, a multilayer architecture is particularly devised for Mining Minds, illustrated in the following figure:
In a nutshell, the data curation layer (DCL) is in charge of processing and persisting the data obtained from the multimodal data sources (MDS), which abstractly defines the possible sources of user health and wellness data. is includes, but is not limited to, data from social networks, questionnaires, wearable biomedical devices or ambient intelligence systems.
The data processed by DCL is primarily used by the information curation layer (ICL) to infer low-level and high-level person-centric information. is information mainly describes the user context and behavior, and, to some extent, their physical, mental and social state. e information extracted by ICL is leveraged by the knowledge curation layer (KCL) to nurture and evolve the health and wellness knowledge primarily created by human experts.
Data, information and knowledge are used by the service curation layer (SCL) to create intelligent health and wellness support services, mostly in the form of smart coaching and support recommendations. All the contents and processes are accommodated in terms of security and privacy by the supporting layer (SL), which also provides analysis of user experience, feedback and trends to guarantee the highest personalization.
Technical details regarding Mining Minds platform can be found in the following publication
Oresti Banos, Muhammad Bilal Amin, Wajahat Ali Khan, Muhammad Afzal, Maqbool Hussain, Byeong Ho Kang and Sungyong Lee, "The Mining Minds Digital Health and Wellness Framework" , BioMedical Engineering OnLine (SCIE, IF:1.43), DOI:10.1186/s12938-016-0179-9, 2016
Big Data Analysis and Modeling
Data acquisition from diverse sources of data in online and offline manner, unified representation and an interoperable and sharable model.
Human Behavior Analysis
User interests and preferences learned from historical and contextual data from emotion, activity, diet and sleep patterns.
Context-aware recommendation generation for the target users according to their needs and situation.
High quality knowledge creation and feedback-based knowledge maintenance.
UI/UX Authoring tool » Provide UX based adaptive UI
Principal Investigator (PI)
- Professor Sungyoung Lee (email@example.com)
Project Manager (PM)
- Dr. Oresti Banos (firstname.lastname@example.org)
Project Manager & Team Lead (PM - TL)
- Dr. Muhammad Bilal Amin (email@example.com)
The code is licensed under the Apache License Version 2.0