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Projekt Druckansicht

Techniken des Organic Computing für die Laufzeit-Selbst-Adaption ubiquitärer, multi-modaler Kontext- und Aktivitätserkennungssysteme

Fachliche Zuordnung Rechnerarchitektur, eingebettete und massiv parallele Systeme
Förderung Förderung von 2015 bis 2019
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 276698135
 
Erstellungsjahr 2020

Zusammenfassung der Projektergebnisse

Using sensors to recognize and monitor human activities has important applications in areas ranging from sports and wellness through maintenance and manufacturing support to health and elderly care. As more and more sensors become available, one would expect such systems to get better and better. Unfortunately, this is often not the case. This is due to the lack of labeled training data for the new sensors, which is costly and difficult to record. In the project OC-SAM, we have developed learning methods that allow new sensors to be integrated into an activity recognition system without or with only very little new labeled training data. The first is based on structural analysis of the feature space that results from adding one or multiple new sensors to an existing system and using appropriate similarity measures to create synthetic labels. The second either exploits the broad availability of easy to label video data to create synthetic labeled training data for a variety of other sensors, or it is based on active learning. An unexpected outcome of the project was the development of an approach that helps to notice the theft of a smart device based on (learned) activities of its owner.

Projektbezogene Publikationen (Auswahl)

 
 

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