mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

[1]  Martin J. O'Connor,et al.  SQWRL: A Query Language for OWL , 2009, OWLED.

[2]  Héctor Pomares,et al.  High-Level Context Inference for Human Behavior Identification , 2015, IWAAL.

[3]  Sungyoung Lee,et al.  Mining human behavior for health promotion , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Bessam Abdulrazak,et al.  Dynamic Domain Model for Micro Context-Aware Adaptation of Applications , 2016, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld).

[5]  Kin K. Leung,et al.  Context-Awareness for Mobile Sensing: A Survey and Future Directions , 2016, IEEE Communications Surveys & Tutorials.

[6]  Tao Gu,et al.  A service-oriented middleware for building context-aware services , 2005, J. Netw. Comput. Appl..

[7]  Prem Prakash Jayaraman,et al.  City Data Fusion: Sensor Data Fusion in the Internet of Things , 2015, Int. J. Distributed Syst. Technol..

[8]  Qin Ni,et al.  A foundational ontology-based model for human activity representation in smart homes , 2016, J. Ambient Intell. Smart Environ..

[9]  Gang Hua,et al.  Joint People, Event, and Location Recognition in Personal Photo Collections Using Cross-Domain Context , 2010, ECCV.

[10]  Erik Blasch,et al.  Context and Fusion: Definitions, Terminology , 2016, Context-Enhanced Information Fusion.

[11]  Harry Chen,et al.  Intelligent Agents Meet the Semantic Web in Smart Spaces , 2004, IEEE Internet Comput..

[12]  Alexander V. Smirnov,et al.  Patterns for context-based knowledge fusion in decision support systems , 2015, Inf. Fusion.

[13]  Weike Pan,et al.  The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL) , 2017, RecSys.

[14]  Adel Alti,et al.  Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments , 2016, Future Internet.

[15]  Sungyoung Lee,et al.  Context Representation and Fusion: Advancements and Opportunities , 2014, Sensors.

[16]  Wilson Jeberson,et al.  Survey of Context Information Fusion for Sensor Networks based Ubiquitous Systems , 2013, ArXiv.

[17]  Ihn-Han Bae,et al.  An ontology-based approach to ADL recognition in smart homes , 2014, Future Gener. Comput. Syst..

[18]  Liming Chen,et al.  Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes , 2014, Future Gener. Comput. Syst..

[19]  Yan Sun,et al.  A Habit-Based SWRL Generation and Reasoning Approach in Smart Home , 2015, 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS).

[20]  Young-Koo Lee,et al.  A Framework for Supervising Lifestyle Diseases Using Long-Term Activity Monitoring , 2012, Sensors.

[21]  Hassina Seridi-Bouchelaghem,et al.  An Ontology-Based Context Model to manage Users Preferences and Conflicts , 2016, Informatica.

[22]  Michael Mosley,et al.  Meal frequency and timing in health and disease , 2014, Proceedings of the National Academy of Sciences.

[23]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[24]  Jesús García,et al.  Context-based Information Fusion: A survey and discussion , 2015, Inf. Fusion.

[25]  J. Cohn,et al.  Expanding the Definition and Classification of Hypertension , 2005, Journal of clinical hypertension.

[26]  Choong Seon Hong,et al.  Human Behavior Analysis by Means of Multimodal Context Mining , 2016, Sensors.

[27]  Héctor Pomares,et al.  Ontology-Based High-Level Context Inference for Human Behavior Identification , 2016, Sensors.

[28]  D. Dunstan,et al.  Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes Association , 2016, Diabetes Care.

[29]  Yu-Liang Chi,et al.  A chronic disease dietary consultation system using OWL-based ontologies and semantic rules , 2015, J. Biomed. Informatics.

[30]  Sungyong Lee,et al.  The Mining Minds digital health and wellness framework , 2016, Biomedical engineering online.

[31]  Liming Chen,et al.  VIPR: A Visual Interface Tool for Programming Semantic Web Rules , 2016, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld).

[32]  Lin Sun,et al.  iCROSS: toward a scalable infrastructure for cross-domain context management , 2012, Personal and Ubiquitous Computing.

[33]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[34]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[35]  U. Ekelund,et al.  Physical Activity and Improvement of Glycemia in Prediabetes by Different Diagnostic Criteria , 2017, The Journal of clinical endocrinology and metabolism.

[36]  Iván Pau,et al.  A Context-Aware System Infrastructure for Monitoring Activities of Daily Living in Smart Home , 2016, J. Sensors.

[37]  Claudio Bettini,et al.  COSAR: hybrid reasoning for context-aware activity recognition , 2011, Personal and Ubiquitous Computing.

[38]  Lin Sun,et al.  The architecture design of a cross-domain context management system , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[39]  Georgios Meditskos,et al.  MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns , 2016, Pervasive Mob. Comput..