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Automated Annotation of Sensor data for Activity Recognition using Deep Learning

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dc.contributor.authorBenndorf, Maik
dc.contributor.authorRingsleben, Frederic
dc.contributor.authorHaenselmann, Thomas
dc.contributor.authorYadav, Bharat
dc.contributor.editorEibl, Maximilian
dc.contributor.editorGaedke, Martin
dc.date.accessioned2017-08-28T23:47:42Z
dc.date.available2017-08-28T23:47:42Z
dc.date.issued2017
dc.description.abstractWithin this work-in-progress, we aim to automate the annotation of Sensor data for generating training data for Activity Recognition (AR) of multiple persons. Usually, the activities are executed and recorded from test persons under the supervision of an instructor, which may influence in many cases the natural behaviour of the test persons and the authenticity of the data. In this work, we suggest how this influence can be reduced and how the Sensor data can be annotated automatically by using video capturing, openpose for extracting human key points and a neuronal network to classify the activities. By automatically annotating the selected activities we show the feasibility of our approach.en
dc.identifier.doi10.18420/in2017_220
dc.identifier.isbn978-3-88579-669-5
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-275
dc.subjectActivity Recognition
dc.subjectAutomated Annotation
dc.subjectSensor data
dc.subjectOpenpose
dc.subjectDeep Learning
dc.titleAutomated Annotation of Sensor data for Activity Recognition using Deep Learningen
gi.citation.endPage2219
gi.citation.startPage2211
gi.conference.date25.-29. September 2017
gi.conference.locationChemnitz
gi.conference.sessiontitleDeep Learning in heterogenen Datenbeständen

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P275 - INFORMATIK 2017