People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information that could provide a fair understanding of the user, but profile information is scattered over different user models. Some researchers in the ubiquitous user modeling community envision the need to share user model's information from heterogeneous sources. In this paper, we address the syntactic and semantic heterogeneity of user models in order to enable user modeling interoperability. We present a dynamic user profile structure based in Simple Knowledge Organization for the Web (SKOS) to provide knowledge representation for ubiquitous user model. We propose a two-tier matching strategy for concept schemas alignment to enable user modeling interoperability. Our proposal is proved in the application scenario of sharing and reusing data in order to deal with overweight and obesity.
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http://dx.doi.org/10.3390/s121013249 | DOI Listing |
bioRxiv
December 2024
Faculty of Biochemistry and Molecular Medicine & Faculty of Medicine, BioIM Unit, University of Oulu, Oulu, FI-90014, Finland.
The ECM is a complex and dynamic meshwork of proteins that forms the framework of all multicellular organisms. Protein interactions within the ECM are critical to building and remodeling the ECM meshwork, while interactions between ECM proteins and cell surface receptors are essential for the initiation of signal transduction and the orchestration of cellular behaviors. Here, we report the development of MatriCom, a web application (https://matrinet.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Institute of Biomedical Engineering and the Department of Electrical Engineering, University of New Brunswick, Fredericton, Canada.
While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
Department of Statistics, University of Florida, Gainesville, FL, 32611, USA.
Background: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data.
View Article and Find Full Text PDFJMIR Med Inform
December 2024
Department of Internal Medicine, University of Michigan, 2800 Plymouth Road, NCRC Building 14, Ann Arbor, MI, 48109, United States, 1 734 430 5359.
Background: Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.
Objective: This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.
Methods: MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020.
Proc ACM Interact Mob Wearable Ubiquitous Technol
November 2024
Northeastern University, USA.
Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection, but frequent interruptions with lengthy surveys on mobile devices can burden users, impacting compliance and data quality. We propose a method that reduces the length of each EMA question set measuring interrelated constructs, with only modest information loss.
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