Human Motion Recognition Using 3D-Skeleton-Data and Neural Networks

This work addresses the recognition of human motion exercises using 3D-skeleton-data and Neural Networks (NN). The examined dataset contains 16 gymnastic motion exercises (e.g. squats, lunges) executed from 21 subjects and captured with the second version of the MicrosoftTM Kinect sensor (Kinect v2)...

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Published inArtificial Intelligence XXXV Vol. 11311; pp. 204 - 209
Main Authors Vox, Jan P., Wallhoff, Frank
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
3D-skeleton-data
Human motion recognition
Neural networks
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Abstract This work addresses the recognition of human motion exercises using 3D-skeleton-data and Neural Networks (NN). The examined dataset contains 16 gymnastic motion exercises (e.g. squats, lunges) executed from 21 subjects and captured with the second version of the MicrosoftTM Kinect sensor (Kinect v2). The NN was trained with eight datasets from eight subjects and tested with 13 unknown datasets. The investigation in this work focuses on the configuration of NNs for human motion recognition. The authors will conclude that a backpropagation NN consisting of 100 neurons, three hidden layers, and a learning rate of 0.001 reaches the best accuracy with 93.8% correct.
AbstractList This work addresses the recognition of human motion exercises using 3D-skeleton-data and Neural Networks (NN). The examined dataset contains 16 gymnastic motion exercises (e.g. squats, lunges) executed from 21 subjects and captured with the second version of the MicrosoftTM Kinect sensor (Kinect v2). The NN was trained with eight datasets from eight subjects and tested with 13 unknown datasets. The investigation in this work focuses on the configuration of NNs for human motion recognition. The authors will conclude that a backpropagation NN consisting of 100 neurons, three hidden layers, and a learning rate of 0.001 reaches the best accuracy with 93.8% correct.
Author Vox, Jan P.
Wallhoff, Frank
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Petridis, Miltos
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Notes This work was supported by EU grants in the INTERREG project Vitale Regionen and by the Jade University of Applied Sciences with the graduate track Jade2Pro.
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PublicationSeriesSubtitle Lecture Notes in Artificial Intelligence
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Snippet This work addresses the recognition of human motion exercises using 3D-skeleton-data and Neural Networks (NN). The examined dataset contains 16 gymnastic...
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StartPage 204
SubjectTerms 3D-skeleton-data
Human motion recognition
Neural networks
Title Human Motion Recognition Using 3D-Skeleton-Data and Neural Networks
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