{"id":133,"date":"2023-05-09T02:30:10","date_gmt":"2023-05-09T02:30:10","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team21\/?page_id=133"},"modified":"2023-12-13T00:25:32","modified_gmt":"2023-12-13T00:25:32","slug":"citations","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team21\/citations\/","title":{"rendered":"References"},"content":{"rendered":"\n<p>[1] Ankur Agarwal and Bill Triggs. Recovering 3d human pose from monocular images. IEEE transactions on pattern analysis and machine intelligence, 28(1):44\u201358, 2005. <br>[2] Robert Behrendt, Amir M Ghaznavi, Meredith Mahan, Susan Craft, and Aamir Siddiqui. Continuous bedside pressure mapping and rates of hospital-associated pressure ulcers in<br>a medical intensive care unit. American Journal of Critical Care, 23(2):127\u2013133, 2014. <br>[3] Dan Berlowitz, C VanDeusen Lukas, V Parker, A Niederhauser, J Silver, C Logan, and E Ayello. Preventing pressure ulcers in hospitals: a toolkit for improving quality of care. Agency for Healthcare Research and Quality, 2011. <br>[4] Joyce M Black, Janet E Cuddigan, Maralyn A Walko, L Alan Didier, Maria J Lander, and Maureen R Kelpe. Medical device related pressure ulcers in hospitalized patients. International wound journal, 7(5):358\u2013365, 2010. <br>[5] Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Rohith Mv, Stefan Stojanov, and James M Rehg. Unsupervised 3d pose estimation with geometric selfsupervision. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 5714\u20135724, 2019<br>[6] Vasileios Choutas, Lea Muller, Chun-Hao P Huang, Siyu Tang, Dimitrios Tzionas, and Michael J Black. Accurate 3d body shape regression using metric and semantic attributes.<br>In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 2718\u20132728, 2022. 6<br>[7] Henry M Clever, Ariel Kapusta, Daehyung Park, Zackory Erickson, Yash Chitalia, and Charles C Kemp. 3d human pose estimation on a configurable bed from a pressure image. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 54\u201361. IEEE, 2018<br>[8] Henry M Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, Karen Liu, and Charles C Kemp. Bodies at rest: 3d human pose and shape estimation from a pressure image using synthetic data. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 6215\u20136224, 2020. 3<br>[9] Henry M Clever, Patrick L Grady, Greg Turk, and Charles C Kemp. Bodypressure-inferring body pose and contact pressure from a depth image. IEEE Transactions on Pattern<br>Analysis and Machine Intelligence, 45(1):137\u2013153, 2022. <br>[10] Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, and Cristian Sminchisescu. Learning complex 3d human self-contact. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1343\u20131351, 2021<br>[11] Gheorghita Ghinea, Fotis Spyridonis, Tacha Serif, and Andrew O Frank. 3-d pain drawings\u2014mobile data collection using a pda. IEEE Transactions on information technology in biomedicine, 12(1):27\u201333, 2008<br>[12] Xuan Gong, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen, David Doermann, and Ziyan Wu. Self-supervised human mesh recovery with cross representation alignment. In European Conference on Computer Vision, pages 212\u2013230. Springer, 2022. 3<br>[13] Lena Gunningberg, Ulrika Poder, and Cheryl Carli. Facilitating student nurses\u2019 learning by real time feedback of positioning to avoid pressure ulcers: evaluation of clinical simulation. J Nurs Educ Practice, 6(1):1\u20138, 2016.<br>[14] Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas, and Michael J Black. Resolving 3d human pose ambiguities with 3d scene constraints. In Proceedings of the IEEE\/CVF international conference on computer vision, pages 2282\u20132292, 2019.<br>[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern<br>recognition, pages 770\u2013778, 2016. <br>[16] Thomas Holleczek, Alex Ruegg, Holger Harms, and Gerhard Troster. Textile pressure sensors for sports applications. In SENSORS, 2010 IEEE, pages 732\u2013737, 2010.<br>[17] Lisa Hultin, Estrid Olsson, Cheryl Carli, and Lena Gunningberg. Pressure mapping in elderly care. Journal of Wound, Ostomy and Continence Nursing, 44(2):142\u2013147, 2017. <br>[18] Debra Jackson, Ahmed M Sarki, Ria Betteridge, and Joanne Brooke. Medical device-related pressure ulcers: a systematic review and meta-analysis. International journal of nursing studies, 92:109\u2013120, 2019. <br>[19] Angjoo Kanazawa, Michael J Black, David W Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7122\u20137131, 2018. <br>[20] Susan A Kayser, Catherine A VanGilder, Elizabeth A Ayello, and Charlie Lachenbruch. Prevalence and analysis of medical device-related pressure injuries: results from the international pressure ulcer prevalence survey. Advances in skin &amp; wound care, 31(6):276, 2018. <br>[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.<br>[22] Muhammed Kocabas, Salih Karagoz, and Emre Akbas. Selfsupervised learning of 3d human pose using multi-view geometry. In Proceedings of the IEEE\/CVF conference on<br>computer vision and pattern recognition, pages 1077\u20131086, 2014<br>[23] Nikos Kolotouros, Georgios Pavlakos, Michael J Black, and Kostas Daniilidis. Learning to reconstruct 3d human pose and shape via model-fitting in the loop. In Proceedings of the IEEE\/CVF international conference on computer vision, pages 2252\u20132261, 2019. <br>[24] Shichao Li, Lei Ke, Kevin Pratama, Yu-Wing Tai, Chi-Keung Tang, and Kwang-Ting Cheng. Cascaded deep monocular 3d human pose estimation with evolutionary training data. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 6173\u20136183, 2020. <br>[25] Kevin Lin, Lijuan Wang, and Zicheng Liu. End-to-end human pose and mesh reconstruction with transformers. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 1954\u20131963, 2021. 3<br>[26] Shuangjun Liu and Sarah Ostadabbas. Seeing under the cover: A physics guided learning approach for in-bed pose estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 236\u20132014. Springer, 2019.<br>[27] Shuangjun Liu and Sarah Ostadabbas. Pressure eye: In-bed contact pressure estimation via contact-less imaging. Medical Image Analysis, 87:102835, 2023.<br>[28] Shuangjun Liu, Xiaofei Huang, Nihang Fu, Cheng Li, Zhongnan Su, and Sarah Ostadabbas. Simultaneously collected multimodal lying pose dataset: Enabling in-bed human pose monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):1106\u20131118, 2022.<\/p>\n\n\n\n<p>[29] Wu Liu, Qian Bao, Yu Sun, and Tao Mei. Recent advances of monocular 2d and 3d human pose estimation: A deep learning perspective. ACM Computing Surveys, 55(4):1\u201341, 2022.<br>[30] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. In Seminal Graphics Papers: Pushing<br>the Boundaries, Volume 2, pages 851\u2013866. 2023.<br>[31] Sam Mansfield, Katia Obraczka, and Shuvo Roy. Pressure injury prevention: A survey. IEEE Reviews in Biomedical Engineering, 13:352\u2013368, 2020. 1, 6<br>[32] Danielle M Minteer, Patsy Simon, Donald P Taylor, Wenyan Jia, Yuecheng Li, Mingui Sun, and J Peter Rubin. Pressure ulcer monitoring platform\u2014a prospective, human subject clinical study to validate patient repositioning monitoring device to prevent pressure ulcers. Advances in wound care, 9(1):28\u201333, 2020. 1<br>[33] Lea Muller, Ahmed AA Osman, Siyu Tang, Chun-Hao P Huang, and Michael J Black. On self-contact and human pose. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 9990\u20139999, 2014.<br>[34] Alejandro Newell, Kaiyu Yang, and Jia Deng. Stacked hourglass networks for human pose estimation. In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14, pages 483\u2013499. Springer, 2016. 3<br>[35] Kaichiro Nishi and Jun Miura. Generation of human depth images with body part labels for complex human pose recognition. Pattern Recognition, 71:402\u2013413, 2017. 2<br>[36] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani,Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 10975\u201310985, 2019. 3<br>[37] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference<br>on computer vision and pattern recognition, pages 652\u2013660, 2014.<br>[38] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information<br>processing systems, 30, 2017. 4<br>[39] Ronald G Scott and Kristen M Thurman. Visual feedback of continuous bedside pressure mapping to optimize effective patient repositioning. Advances in wound care, 3(5):376\u2013382, 2014. 1<br>[40] Devdip Sen, John McNeill, Yitzhak Mendelson, Raymond Dunn, and Kelli Hickle. A new vision for preventing pressure ulcers: wearable wireless devices could help solve<br>a common-and serious-problem. IEEE pulse, 9(6):28\u201331, 2014.<br>[41] Fotios Spyridonis and Gheorghita Ghinea. 3-d pain drawings and seating pressure maps: Relationships and challenges. IEEE Transactions on Information Technology in<br>Biomedicine, 15(3):409\u2013415, 2011. 3<br>[42] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3d human mesh from monocular images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 3<br>[43] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1653\u20131660, 2014. 3<br>[44] Gurjot S Walia, Alison L Wong, Andrea Y Lo, Gina A Mackert, Hannah M Carl, Rachel A Pedreira, Ricardo Bello, Carla S Aquino, William V Padula, and Justin M Sacks. Efficacy of monitoring devices in support of prevention of pressure injuries: systematic review and meta-analysis. Advances in skin &amp; wound care, 29(12):567\u2013574, 2016. 1<br>[45] Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei. 3d human pose machines with self-supervised learning. IEEE transactions on pattern analysis and machine<br>intelligence, 42(5):1069\u20131082, 2019. 3<\/p>\n","protected":false},"excerpt":{"rendered":"<p>[1] Ankur Agarwal and Bill Triggs. Recovering 3d human pose from monocular images. IEEE transactions on pattern analysis and machine intelligence, 28(1):44\u201358, 2005. [2] Robert Behrendt, Amir M Ghaznavi, Meredith Mahan, Susan Craft, and Aamir Siddiqui. Continuous bedside pressure mapping and rates of hospital-associated pressure ulcers ina medical intensive care unit. American Journal of Critical &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team21\/citations\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;References&#8221;<\/span><\/a><\/p>\n","protected":false},"author":188,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-133","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>References - Multimodal Visual Learning for Pressure Ulcer Prevention<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team21\/citations\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"References - Multimodal Visual Learning for Pressure Ulcer Prevention\" \/>\n<meta property=\"og:description\" content=\"[1] Ankur Agarwal and Bill Triggs. 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