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.
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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Acknowledgements
The authors gratefully acknowledge the contribution of Jannik Flessner, Johannes Hurka, Tobias Theuerkauff, Jana Tessmer and Yves Wagner.
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Vox, J.P., Wallhoff, F. (2018). Human Motion Recognition Using 3D-Skeleton-Data and Neural Networks. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_19
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