{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T20:34:19Z","timestamp":1771360459432,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T00:00:00Z","timestamp":1581465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["313421352"],"award-info":[{"award-number":["313421352"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable to or even better for very short anticipation times (&lt;0.4 s) than a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of previous states and delays. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence it is of a generic nature.<\/jats:p>","DOI":"10.3390\/s20040976","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised and Generic Short-Term Anticipation of Human Body Motions"],"prefix":"10.3390","volume":"20","author":[{"given":"Kristina","family":"Enes","sequence":"first","affiliation":[{"name":"Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4127-3529","authenticated-orcid":false,"given":"Hassan","family":"Errami","sequence":"additional","affiliation":[{"name":"Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moritz","family":"Wolter","sequence":"additional","affiliation":[{"name":"Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Krake","sequence":"additional","affiliation":[{"name":"HdM Stuttgart, 70569 Stuttgart, Germany"},{"name":"Fachbereich Informatik, University of Stuttgart, 70569 Stuttgart, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Eberhardt","sequence":"additional","affiliation":[{"name":"HdM Stuttgart, 70569 Stuttgart, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5624-3368","authenticated-orcid":false,"given":"Andreas","family":"Weber","sequence":"additional","affiliation":[{"name":"Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Levine, S., Felsen, P., and Malik, J. (2015, January 13\u201316). Recurrent network models for human dynamics. Proceedings of the International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.494"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Martinez, J., Black, M.J., and Romero, J. (2017, January 12\u201316). On human motion prediction using recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.497"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gui, L.Y., Wang, Y.X., Liang, X., and Moura, J.M. (2018, January 7\u201313). Adversarial geometry-aware human motion prediction. Proceedings of the European Conference on Computer Vision, Paris, France.","DOI":"10.1007\/978-3-030-01225-0_48"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., and Saxena, A. (2016, January 27\u201330). Structural-RNN: Deep learning on spatio-temporal graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, Z., Sun Lee, W., and Hee Lee, G. (2018, January 18\u201322). Convolutional sequence to sequence model for human dynamics. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00548"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Auli, M., and Grangier, D. (2019). Modeling Human Motion with Quaternion-based Neural Networks. Int. J. Comput. Vis.","DOI":"10.1007\/s11263-019-01245-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abu Farha, Y., Richard, A., and Gall, J. (2018, January 18\u201322). When will you do what?\u2013Anticipating temporal occurrences of activities. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00560"},{"key":"ref_8","unstructured":"Ruiz, A.H., Gall, J., and Moreno-Noguer, F. (November, January 27). Human Motion Prediction via Spatio-Temporal Inpainting. Proceedings of the International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gopalakrishnan, A., Mali, A., Kifer, D., Giles, L., and Ororbia, A.G. (2019, January 16\u201320). A neural temporal model for human motion prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01239"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kutz, J.N., Brunton, S.L., Brunton, B.W., and Proctor, J.L. (2016). Dynamic Mode Decomposition, Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9781611974508"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1017\/S0022112010001217","article-title":"Dynamic Mode Decomposition of numerical and experimental data","volume":"656","author":"Schmid","year":"2008","journal-title":"J. Fluid Mech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3934\/jcd.2014.1.391","article-title":"On dynamic mode decomposition: Theory and applications","volume":"1","author":"Tu","year":"2014","journal-title":"J. Comput. Dyn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.patrec.2019.02.010","article-title":"Supervised dynamic mode decomposition via multitask learning","volume":"122","author":"Fujii","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neunet.2019.04.020","article-title":"Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables","volume":"117","author":"Fujii","year":"2019","journal-title":"Neural Net."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"024103","DOI":"10.1063\/1.4863670","article-title":"Sparsity-promoting dynamic mode decomposition","volume":"26","author":"Jovanovic","year":"2014","journal-title":"Phy. Fluids"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peddinti, V., Povey, D., and Khudanpur, S. (2015, January 6\u201310). A time delay neural network architecture for efficient modeling of long temporal contexts. Proceedings of the 16th Annual Conference of the International Speech Communication Association, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-647"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012076","DOI":"10.1088\/1742-6596\/1229\/1\/012076","article-title":"Deeper Time Delay Neural Networks for Effective Acoustic Modelling","volume":"1229","author":"Huang","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","first-page":"473","article-title":"LSTM can solve hard long time lag problems","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Adv. Neural Infor. Proc. Syst."},{"key":"ref_20","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_21","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2020, February 01). Trellis Networks for Sequence Modeling. International Conference on Learning Representations (ICLR). Available online: https:\/\/openreview.net\/forum?id=HyeVtoRqtQ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1073\/pnas.1517384113","article-title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","volume":"113","author":"Brunton","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","unstructured":"Krake, T., Weiskopf, D., and Eberhardt, B. (2019). Dynamic Mode Decomposition: Theory and Data Reconstruction. arXiv."},{"key":"ref_24","first-page":"366","article-title":"Detecting strange attractors in turbulence","volume":"Volume 898","author":"Rand","year":"1981","journal-title":"Dynamical Systems and Turbulence"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/BF01053745","article-title":"Embedology","volume":"65","author":"Sauer","year":"1991","journal-title":"J. Stat. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","article-title":"Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments","volume":"36","author":"Ionescu","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Wolter, M., and Yao, A. (2018, January 3\u20138). Gated Complex Recurrent Neural Networks. Proceedings of the Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mao, W., Liu, M., Salzmann, M., and Li, H. (2019, January 28\u201329). Learning Trajectory Dependencies for Human Motion Prediction. Proceedings of the International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00958"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1966394.1966397","article-title":"Motion Reconstruction Using Sparse Accelerometer Data","volume":"30","author":"Tautges","year":"2011","journal-title":"ACM Trans. Graph."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Takeishi, N., Kawahara, Y., Tabei, Y., and Yairi, T. (2017, January 19\u201325). Bayesian Dynamic Mode Decomposition. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/392"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Everitt, T., Lea, G., and Hutter, M. (2018, January 13\u201319). AGI Safety Literature Review. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI\u201918), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/768"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:57:03Z","timestamp":1760173023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,12]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20040976"],"URL":"https:\/\/doi.org\/10.3390\/s20040976","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,12]]}}}