Tiered Data Integration for Mobile Health Systems

One of the most promising instantiations of the Internet of Things (IoT) are mobile health (mHealth) systems, which promise to deliver intelligent health monitoring and assisted living as well as advanced and integrated health services. To realize the full potential of these services, fragmented and heterogeneous data that is generated by different segments of the system need to be consolidated in order to support high-quality processes. This paper proposes a tiered data integration scheme for mHealth systems that works on the schema, entity, and event levels. The proposed scheme incorporates an algorithm that merges and ranks sensor streams for schema integration and event identification, and performs contextual record registration and deduplication for entity resolution. We tested the proposed integration scheme on two sets of sensor-based mHealth data related to human activity recognition. Preliminary results show that the proposed integration scheme contributes to enhancements in event identification precision compared to the classification performance of separate datasets produced within the same mHealth system.

[1]  Héctor Pomares,et al.  mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications , 2014, IWAAL.

[2]  Zoubin Ghahramani,et al.  Bayesian correlated clustering to integrate multiple datasets , 2012, Bioinform..

[3]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[4]  Michael Stonebraker,et al.  Data Curation at Scale: The Data Tamer System , 2013, CIDR.

[5]  Arthur W. Toga,et al.  Medical data transformation using rewriting , 2015, Front. Neuroinform..

[6]  Araceli Sanchis,et al.  Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors , 2013, Sensors.

[7]  Zhigang Wen,et al.  A scalable Internet of Things Lean Data provision architecture based on ontology , 2011, 2011 IEEE GCC Conference and Exhibition (GCC).

[8]  Joanna F Dipnall,et al.  Data Integration Protocol In Ten-steps (DIPIT): a new standard for medical researchers. , 2014, Methods.

[9]  Arie Hasman,et al.  Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage. , 2011, Journal of clinical epidemiology.

[10]  Todd D. Millstein,et al.  Navigational Plans For Data Integration , 1999, AAAI/IAAI.

[11]  Arthur W. Toga,et al.  A schema-matching tool for Alzheimer's disease data integration , 2014, BCB.

[12]  Chi Harold Liu,et al.  Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things , 2013, IEEE Sensors Journal.

[13]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[14]  Klaus-Dieter Thoben,et al.  A Service-oriented, Semantic Approach to Data Integration for an Internet of Things Supporting Autonomous Cooperating Logistics Processes , 2011, Architecting the Internet of Things.