Using machine learning to determine attention towards public displays from skeletal data

dc.contributor.author	Lacher, Jonas
dc.contributor.author	Bieschke, Laura
dc.contributor.author	Michalowski, Florian
dc.contributor.author	Münch, Johanes
dc.date.accessioned	2023-08-24T06:24:28Z
dc.date.available	2023-08-24T06:24:28Z
dc.date.issued	2023
dc.description.abstract	We develop a classifier model trained to analyze anonymized skeletal data of passers-by at interactive public displays to determine whether an interaction has occured. The test setup and data collection methods are described. The skeletal data is preprocessed to highlight more relevant bodyparts. The performance of the finished model will be evaluated statistically and compared to approaches using human observers from other research.	de
dc.identifier.doi	10.18420/muc2023-mci-ws13-293
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/42126
dc.publisher	GI
dc.relation.ispartof	Mensch und Computer 2023 - Workshopband
dc.relation.ispartofseries	Mensch und Computer
dc.subject	machine learning
dc.subject	attention
dc.subject	skeletal data
dc.subject	public display
dc.subject	evaluation
dc.title	Using machine learning to determine attention towards public displays from skeletal data	de
dc.type	Text/Workshop Paper
gi.conference.date	3.-6. September 2023
gi.conference.location	Rapperswil
gi.conference.sessiontitle	MCI-WS13: Methods and Tools for (Semi-)Automated Evaluation in Long-Term In-the-Wild Deployment Studies

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