{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:40:40Z","timestamp":1775598040509,"version":"3.50.1"},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2018,12,4]],"date-time":"2018-12-04T00:00:00Z","timestamp":1543881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n                    We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330 000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.\n                    <jats:sup>1<\/jats:sup>\n                  <\/jats:p>","DOI":"10.1145\/3272127.3275108","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T14:16:10Z","timestamp":1543414570000},"page":"1-15","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":284,"title":["Deep inertial poser"],"prefix":"10.1145","volume":"37","author":[{"given":"Yinghao","family":"Huang","sequence":"first","affiliation":[{"name":"Max Planck Insitute for Intelligent Systems, T\u00fcbingen, Germany"}]},{"given":"Manuel","family":"Kaufmann","sequence":"additional","affiliation":[{"name":"Advanced Interactive Technologies Lab, ETH Z\u00fcrich, Switzerland"}]},{"given":"Emre","family":"Aksan","sequence":"additional","affiliation":[{"name":"Advanced Interactive Technologies Lab, ETH Z\u00fcrich, Switzerland"}]},{"given":"Michael J.","family":"Black","sequence":"additional","affiliation":[{"name":"Max Planck Insitute for Intelligent Systems, T\u00fcbingen, Germany"}]},{"given":"Otmar","family":"Hilliges","sequence":"additional","affiliation":[{"name":"Advanced Interactive Technologies Lab, ETH Z\u00fcrich, Switzerland"}]},{"given":"Gerard","family":"Pons-Moll","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Informatics, Saarbr\u00fccken, Germany"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Unifying Motion Capture Datasets by Automatically Solving for Full-body Shape and Motion. In preparation","year":"2018","unstructured":"2018. Unifying Motion Capture Datasets by Automatically Solving for Full-body Shape and Motion. In preparation ( 2018 ). 2018. Unifying Motion Capture Datasets by Automatically Solving for Full-body Shape and Motion. In preparation (2018)."},{"key":"e_1_2_2_2_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). https:\/\/linproxy.fan.workers.dev:443\/https\/www.tensorflow.org\/Software available from tensorflow.org. Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). https:\/\/linproxy.fan.workers.dev:443\/https\/www.tensorflow.org\/Software available from tensorflow.org."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298751"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00875"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2998559.2998564"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33783-3_46"},{"key":"e_1_2_2_7_1","volume-title":"Black","author":"Bogo Federica","year":"2016","unstructured":"Federica Bogo , Angjoo Kanazawa , Christoph Lassner , Peter Gehler , Javier Romero , and Michael J . Black . 2016 . Keep it SMPL : Automatic Estimation of 3D Human Pose and Shape from a Single Image. In Computer Vision - ECCV 2016 (Lecture Notes in Computer Science). Springer International Publishing , 561--578. Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. 2016. Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. In Computer Vision - ECCV 2016 (Lecture Notes in Computer Science). Springer International Publishing, 561--578."},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/794191.794776"},{"key":"e_1_2_2_9_1","volume-title":"Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1611.08050","author":"Cao Zhe","year":"2016","unstructured":"Zhe Cao , Tomas Simon , Shih-En Wei , and Yaser Sheikh . 2016. Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1611.08050 ( 2016 ). Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2016. Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1611.08050 (2016)."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073248"},{"key":"e_1_2_2_11_1","unstructured":"Xianjie Chen and Alan L Yuille. 2014. Articulated pose estimation by a graphical model with image dependent pairwise relations. In NIPS. 1736--1744. Xianjie Chen and Alan L Yuille. 2014. Articulated pose estimation by a graphical model with image dependent pairwise relations. In NIPS. 1736--1744."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766945"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360697"},{"key":"e_1_2_2_14_1","volume-title":"Guide to the carnegie mellon university multimodal activity (cmu-mmac) database","author":"la Torre Fernando De","year":"2008","unstructured":"Fernando De la Torre , Jessica Hodgins , Adam Bargteil , Xavier Martin , Justin Macey , Alex Collado , and Pep Beltran . 2008. Guide to the carnegie mellon university multimodal activity (cmu-mmac) database . Robotics Institute ( 2008 ), 135. Fernando De la Torre, Jessica Hodgins, Adam Bargteil, Xavier Martin, Justin Macey, Alex Collado, and Pep Beltran. 2008. Guide to the carnegie mellon university multimodal activity (cmu-mmac) database. Robotics Institute (2008), 135."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925969"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2557779"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919834"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33783-3_53"},{"key":"e_1_2_2_19_1","volume-title":"Learning Human Motion Models for Long-term Predictions. arXiv preprint arXiv:1704.02827","author":"Ghosh Partha","year":"2017","unstructured":"Partha Ghosh , Jie Song , Emre Aksan , and Otmar Hilliges . 2017. Learning Human Motion Models for Long-term Predictions. arXiv preprint arXiv:1704.02827 ( 2017 ). Partha Ghosh, Jie Song, Emre Aksan, and Otmar Hilliges. 2017. Learning Human Motion Models for Long-term Predictions. arXiv preprint arXiv:1704.02827 (2017)."},{"key":"e_1_2_2_20_1","volume-title":"Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks","author":"Hannink Julius","year":"2016","unstructured":"Julius Hannink , Thomas Kautz , Cristian Pasluosta , Karl-Gunter Gassmann , Jochen Klucken , and Bjoern Eskofier . 2016. Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks . IEEE Journal of Biomedical and Health Informatics ( 2016 ), 85--93. Julius Hannink, Thomas Kautz, Cristian Pasluosta, Karl-Gunter Gassmann, Jochen Klucken, and Bjoern Eskofier. 2016. Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics (2016), 85--93."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.141"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073663"},{"key":"e_1_2_2_25_1","volume-title":"International Conference on 3D Vision (3DV). 421--430","author":"Huang Yinghao","unstructured":"Yinghao Huang , Federica Bogo , Christoph Lassner , Angjoo Kanazawa , Peter V. Gehler , Javier Romero , Ijaz Akhter , and Michael J. Black . 2017. Towards Accurate Marker-less Human Shape and Pose Estimation over Time . In International Conference on 3D Vision (3DV). 421--430 . Yinghao Huang, Federica Bogo, Christoph Lassner, Angjoo Kanazawa, Peter V. Gehler, Javier Romero, Ijaz Akhter, and Michael J. Black. 2017. Towards Accurate Marker-less Human Shape and Pose Estimation over Time. In International Conference on 3D Vision (3DV). 421--430."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.248"},{"key":"e_1_2_2_27_1","volume-title":"End-to-end Recovery of Human Shape and Pose. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society.","author":"Kanazawa Angjoo","year":"2018","unstructured":"Angjoo Kanazawa , Michael J. Black , David W. Jacobs , and Jitendra Malik . 2018 . End-to-end Recovery of Human Shape and Pose. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society. Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. 2018. End-to-end Recovery of Human Shape and Pose. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society."},{"key":"e_1_2_2_28_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1944745.1944768"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661273"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818013"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00058"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.497"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00024"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073596"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/s17112589"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298631"},{"key":"e_1_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Alejandro Newell Kaiyu Yang and Jia Deng. 2016. Stacked hourglass networks for human pose estimation. In ECCV. 483--499. Alejandro Newell Kaiyu Yang and Jia Deng. 2016. Stacked hourglass networks for human pose estimation. In ECCV. 483--499.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354910"},{"key":"e_1_2_2_40_1","volume-title":"Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. In International Conference on 3D Vision (3DV).","author":"Omran Mohamed","year":"2018","unstructured":"Mohamed Omran , Christoph Lassner , Gerard Pons-Moll , Peter Gehler , and Bernt Schiele . 2018 . Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. In International Conference on 3D Vision (3DV). Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter Gehler, and Bernt Schiele. 2018. Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. In International Conference on 3D Vision (3DV)."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126375"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540153"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073711"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766993"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0818-9"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980235"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.94"},{"key":"e_1_2_2_49_1","volume-title":"Xsen Technologies","author":"Roetenberg Daniel","year":"2007","unstructured":"Daniel Roetenberg , Henk Luinge , and Per Slycke . 2007 . Moven: Full 6dof human motion tracking using miniature inertial sensors . Xsen Technologies , December (2007). Daniel Roetenberg, Henk Luinge, and Per Slycke. 2007. Moven: Full 6dof human motion tracking using miniature inertial sensors. Xsen Technologies, December (2007)."},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2016.09.002"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10470-1_14"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2398356.2398381"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0273-6"},{"key":"e_1_2_2_55_1","volume-title":"Proceedings of the 2008 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA '08)","author":"Slyper R.","unstructured":"R. Slyper and J. Hodgins . 2008. Action capture with accelerometers . In Proceedings of the 2008 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA '08) . Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 193--199. https:\/\/linproxy.fan.workers.dev:443\/http\/dl.acm.org\/citation.cfm?id=1632592.1632620 R. Slyper and J. Hodgins. 2008. Action capture with accelerometers. In Proceedings of the 2008 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA '08). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 193--199. https:\/\/linproxy.fan.workers.dev:443\/http\/dl.acm.org\/citation.cfm?id=1632592.1632620"},{"key":"e_1_2_2_56_1","volume-title":"Model-based multiple view reconstruction of people. In null","author":"Starck Jonathan","unstructured":"Jonathan Starck and Adrian Hilton . 2003. Model-based multiple view reconstruction of people. In null . IEEE , 915. Jonathan Starck and Adrian Hilton. 2003. Model-based multiple view reconstruction of people. In null. IEEE, 915."},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126338"},{"key":"e_1_2_2_58_1","volume-title":"IEEE Conf. on Computer Vision and Pattern Recognition. IEEE Conf. on Computer Vision and Pattern Recognition. CVPR Oral.","author":"Tao Yu","year":"2018","unstructured":"Yu Tao , Zerong Zheng , Kaiwen Guo , Jianhui Zhao , Dai Quionhai , Hao Li , Gerard Pons-Moll , and Yebin Liu . 2018 . DoubleFusion: Real-time Capture of Human Performance with Inner Body Shape from a Depth Sensor , In IEEE Conf. on Computer Vision and Pattern Recognition. IEEE Conf. on Computer Vision and Pattern Recognition. CVPR Oral. Yu Tao, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Dai Quionhai, Hao Li, Gerard Pons-Moll, and Yebin Liu. 2018. DoubleFusion: Real-time Capture of Human Performance with Inner Body Shape from a Depth Sensor, In IEEE Conf. on Computer Vision and Pattern Recognition. IEEE Conf. on Computer Vision and Pattern Recognition. CVPR Oral."},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/1966394.1966397"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354668"},{"key":"e_1_2_2_61_1","volume-title":"Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation. arXiv preprint arXiv:1611.05708","author":"Tekin Bugra","year":"2016","unstructured":"Bugra Tekin , Pablo M\u00e1rquez-Neila , Mathieu Salzmann , and Pascal Fua . 2016. Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation. arXiv preprint arXiv:1611.05708 ( 2016 ). Bugra Tekin, Pablo M\u00e1rquez-Neila, Mathieu Salzmann, and Pascal Fua. 2016. Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation. arXiv preprint arXiv:1611.05708 (2016)."},{"key":"e_1_2_2_62_1","unstructured":"Jonathan J Tompson Arjun Jain Yann LeCun and Christoph Bregler. 2014. Joint training of a convolutional network and a graphical model for human pose estimation. In NIPS. 1799--1807. Jonathan J Tompson Arjun Jain Yann LeCun and Christoph Bregler. 2014. Joint training of a convolutional network and a graphical model for human pose estimation. In NIPS. 1799--1807."},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.14"},{"key":"e_1_2_2_65_1","volume-title":"WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499","author":"van den Oord A\u00e4ron","year":"2016","unstructured":"A\u00e4ron van den Oord , Sander Dieleman , Heiga Zen , Karen Simonyan , Oriol Vinyals , Alex Graves , Nal Kalchbrenner , Andrew W. Senior , and Koray Kavukcuoglu . 2016. WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499 ( 2016 ). arXiv:1609.03499 https:\/\/linproxy.fan.workers.dev:443\/http\/arxiv.org\/abs\/1609.03499 A\u00e4ron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu. 2016. WaveNet: A Generative Model for Raw Audio. CoRR abs\/1609.03499 (2016). arXiv:1609.03499 https:\/\/linproxy.fan.workers.dev:443\/http\/arxiv.org\/abs\/1609.03499"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276421"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"e_1_2_2_68_1","first-page":"8","article-title":"Human Pose Estimation from Video and IMUs","volume":"38","author":"von Marcard Timo","year":"2016","unstructured":"Timo von Marcard , Gerard Pons-Moll , and Bodo Rosenhahn . 2016 . Human Pose Estimation from Video and IMUs . IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 38 , 8 (aug 2016), 1533--1547. Timo von Marcard, Gerard Pons-Moll, and Bodo Rosenhahn. 2016. Human Pose Estimation from Video and IMUs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 38, 8 (aug 2016), 1533--1547.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)"},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13131"},{"key":"e_1_2_2_70_1","volume-title":"Deep learning for sensor-based activity recognition: A survey. arXiv preprint arXiv:1707.03502","author":"Wang Jindong","year":"2017","unstructured":"Jindong Wang , Yiqiang Chen , Shuji Hao , Xiaohui Peng , and Lisha Hu. 2017. Deep learning for sensor-based activity recognition: A survey. arXiv preprint arXiv:1707.03502 ( 2017 ). Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2017. Deep learning for sensor-based activity recognition: A survey. arXiv preprint arXiv:1707.03502 (2017)."},{"key":"e_1_2_2_71_1","doi-asserted-by":"crossref","unstructured":"Shih-En Wei Varun Ramakrishna Takeo Kanade and Yaser Sheikh. 2016. Convolutional pose machines. In CVPR. 4724--4732. Shih-En Wei Varun Ramakrishna Takeo Kanade and Yaser Sheikh. 2016. Convolutional pose machines. In CVPR. 4724--4732.","DOI":"10.1109\/CVPR.2016.511"},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366207"},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.537"},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601165"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3272127.3275108","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3272127.3275108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:58:32Z","timestamp":1775285912000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3272127.3275108"}},"subtitle":["learning to reconstruct human pose from sparse inertial measurements in real time"],"short-title":[],"issued":{"date-parts":[[2018,12,4]]},"references-count":74,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,12,31]]}},"alternative-id":["10.1145\/3272127.3275108"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/3272127.3275108","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,4]]},"assertion":[{"value":"2018-12-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}