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Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning

Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning

Volkhard Klinger
Copyright: © 2022 |Volume: 9 |Issue: 2 |Pages: 22
EISBN13: 9781799862277|ISSN: 2771-3687|EISSN: 2771-3679|DOI: 10.4018/IJPHIMT.315770
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MLA

Klinger, Volkhard. "Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning." IJPHIMT vol.9, no.2 2022: pp.1-22. http://doi.org/10.4018/IJPHIMT.315770

APA

Klinger, V. (2022). Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning. International Journal of Practical Healthcare Innovation and Management Techniques (IJPHIMT), 9(2), 1-22. http://doi.org/10.4018/IJPHIMT.315770

Chicago

Klinger, Volkhard. "Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning," International Journal of Practical Healthcare Innovation and Management Techniques (IJPHIMT) 9, no.2: 1-22. http://doi.org/10.4018/IJPHIMT.315770

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Abstract

The internet of things is enabling new applications, especially in the field of biomedical systems. Such internet of things systems can not only use an existing infrastructure, but also build their own network for data exchange. By linking multiple distributed sensors, complex interpretations of data and identification of scenarios can be realized based on sensor fusion. This allows new relationships to be captured and interpreted. Data fusion is thus a key concept for object, motion, and scenario identification based on sensor values. Here, the authors first introduce specific applications of sensor fusion. Driven by the increase of pandemic work from home, this paper describes an IoT-based posture monitoring. The selection of different sensor types can be made on an application-specific basis using the flexible IoT platform, providing a toolbox. Second focus is the presentation of a concept for the integration of process models into the data fusion process. This model-based data fusion extends the concepts of the data fusion information group (DFIG) model.

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