ABSTRACT
One promising way to tackle healthcare challenges due to demographic change lies in the development of user-tailored AAL technologies. Video-based AAL technologies have the potential to provide rich information - in particular about accidents such as falls. However, as visual AAL is designed to record some parts of daily life at home, privacy concerns may comprise recordings in unwanted appearances and especially while being nude. Here, collaborative research is necessary to enable the development of user-tailored (visual) AAL technologies taking into account future users’ needs and concerns. This article presents an interdisciplinary collaboration investigating perceptions of nudity from a social perspective, and developing solutions on nudity detection from a technical perspective. Focusing on first empirical insights and a proposed methodology for level-based nudity detection, this article concludes with interdisciplinary learnings, derived guidelines, and implications for future collaborative research.
- Rigan Ap-Apid. 2005. An algorithm for nudity detection. In 5th Philippine Computing Science Congress. 201–205.Google Scholar
- Katrin Arning and Martina Ziefle. 2015. “Get that camera out of my house!” conjoint measurement of preferences for video-based healthcare monitoring systems in private and public places. In International Conference on Smart Homes and Health Telematics. Springer, Cham., 152–164.Google ScholarCross Ref
- Stephanie Blackman, Claudine Matlo, Charisse Bobrovitskiy, Ashley Waldoch, Mei Lan Fang, Piper Jackson, Alex Mihailidis, Louise Nygård, Arlene Astell, and Andrew Sixsmith. 2016. Ambient assisted living technologies for aging well: a scoping review. J. Intell. Sys. 25, 1 (2016), 55–69.Google ScholarCross Ref
- Carlos Caetano, Sandra Avila, William Robson Schwartz, Silvio Jamil F Guimarães, and Arnaldo de A Araújo. 2016. A mid-level video representation based on binary descriptors: A case study for pornography detection. Neurocomput. 213(2016), 102–114.Google ScholarDigital Library
- Kelly Caine, Selma Šabanovic, and Mary Carter. 2012. The effect of monitoring by cameras and robots on the privacy enhancing behaviors of older adults. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction. ACM/IEEE, Boston, USA, 343–350.Google ScholarDigital Library
- Davide Calvaresi, Daniel Cesarini, Paolo Sernani, Mauro Marinoni, Aldo Franco Dragoni, and Arnon Sturm. 2017. Exploring the ambient assisted living domain: a systematic review. J. Ambient Intell. Humaniz. Comput. 8, 2 (2017), 239–257.Google ScholarCross Ref
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 4 (2017), 834–848.Google ScholarCross Ref
- Pau Climent-Pérez, Susanna Spinsante, Alex Mihailidis, and Francisco Florez-Revuelta. 2020. A review on video-based active and assisted living technologies for automated lifelogging. Expert Syst. Appl. 139(2020), 112847.Google ScholarDigital Library
- Enric Corona, Guillem Alenya, Antonio Gabas, and Carme Torras. 2018. Active garment recognition and target grasping point detection using deep learning. Pattern Recognit. 74(2018), 629–641.Google ScholarDigital Library
- Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, and Gianpiero Francesca. 2019. Toyota smarthome: Real-world activities of daily living. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 833–842.Google ScholarCross Ref
- Humira Ehrari, Frank Ulrich, and Henning Boje Andersen. 2020. Concerns and trade-offs in information technology acceptance: The balance between the requirement for privacy and the desire for safety. Commun. Assoc. Inf. Syst. 47, 1 (2020), 46.Google Scholar
- Rachel L Finn, David Wright, and Michael Friedewald. 2013. Seven types of privacy. In European data protection: coming of age. Springer, 3–32.Google Scholar
- David A Forsyth and Margaret M Fleck. 1999. Automatic detection of human nudes. Int. J. Comp. Vis. 32, 1 (1999), 63–77.Google ScholarDigital Library
- Yanjun Fu and Weiqiang Wang. 2011. Fast and effectively identify pornographic images. In 2011 Seventh International Conference on Computational Intelligence and Security. IEEE, 1122–1126.Google ScholarDigital Library
- Rıza Alp Güler, Natalia Neverova, and Iasonas Kokkinos. 2018. Densepose: Dense human pose estimation in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7297–7306.Google ScholarCross Ref
- Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, and Jue Wang. 2019. Semi-supervised skin detection by network with mutual guidance. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2111–2120.Google ScholarCross Ref
- Simon Himmel and Martina Ziefle. 2016. Smart home medical technologies: users’ requirements for conditional acceptance. i-com 15, 1 (2016), 39–50.Google Scholar
- Christina Jaschinski and Somaya Ben Allouch. 2015. An extended view on benefits and barriers of ambient assisted living solutions. International Journal on Advances in Life Sciences. 7, 1-2(2015), 40–53.Google Scholar
- Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, and Serge Belongie. 2020. Fashionpedia: Ontology, segmentation, and an attribute localization dataset. In European Conference on Computer Vision. Springer, 316–332.Google ScholarDigital Library
- Xin Jin, Yuhui Wang, and Xiaoyang Tan. 2018. Pornographic image recognition via weighted multiple instance learning. IEEE Trans. Cybern. 49, 12 (2018), 4412–4420.Google ScholarCross Ref
- Saadi Lahlou. 2008. Cognitive technologies, social science and the three-layered leopardskin of change. Soc. Sci. Inform. 47, 3 (2008), 227–251.Google ScholarCross Ref
- Saadi Lahlou. 2008. Identity, social status, privacy and face-keeping in digital society. Soc. Sci. Inform. 47, 3 (2008), 299–330.Google ScholarCross Ref
- Nolwenn Lapierre, Alain St-Arnaud, Jean Meunier, and Jacqueline Rousseau. 2020. Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study. J. Enabling Technol. 14, 4 (2020), 253–271.Google ScholarCross Ref
- Ana PB Lopes, Sandra EF de Avila, Anderson NA Peixoto, Rodrigo S Oliveira, and Arnaldo de A Araújo. 2009. A bag-of-features approach based on hue-sift descriptor for nude detection. In 2009 17th European Signal Processing Conference. IEEE, 1552–1556.Google Scholar
- Mohammad Reza Mahmoodi and Sayed Masoud Sayedi. 2016. A comprehensive survey on human skin detection. Int. J. Image Graph. Signal Process. 8, 5 (2016), 1.Google ScholarCross Ref
- Marco Manfredi, Costantino Grana, Simone Calderara, and Rita Cucchiara. 2014. A complete system for garment segmentation and color classification. Mach. Vis. Appl. 25, 4 (2014), 955–969.Google ScholarDigital Library
- Danilo Coura Moreira and Joseana Macêdo Fechine. 2018. A machine learning-based forensic discriminator of pornographic and bikini images. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janeiro, Brasil, 1–8.Google ScholarCross Ref
- José Ramón Padilla-López, Alexandros Andre Chaaraoui, Feng Gu, and Francisco Flórez-Revuelta. 2015. Visual privacy by context: proposal and evaluation of a level-based visualisation scheme. Sensors 15, 6 (2015), 12959–12982.Google ScholarCross Ref
- Sebastiaan TM Peek, Eveline JM Wouters, Joost Van Hoof, Katrien G Luijkx, Hennie R Boeije, and Hubertus JM Vrijhoef. 2014. Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inform. 83, 4 (2014), 235–248.Google ScholarCross Ref
- B. Devi Prasad. 2008. Content analysis. Res. Method. Soc. Work 5(2008), 1–20.Google Scholar
- Steven Prentice-Dunn and Ronald W Rogers. 1986. Protection motivation theory and preventive health: Beyond the health belief model. Health Educ. Res. 1, 3 (1986), 153–161.Google ScholarCross Ref
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.Google ScholarCross Ref
- Peter Schaar. 2010. Privacy by design. Identity Inform. Soc. 3, 2 (2010), 267–274.Google ScholarCross Ref
- Evan Shelhamer, Jonathan Long, and Trevor Darrell. 2016. Fully convolutional networks for semantic segmentation. (2016).Google Scholar
- Lei Sui, Jing Zhang, Li Zhuo, and YC Yang. 2012. Research on pornographic images recognition method based on visual words in a compressed domain. IET Image Process. 6, 1 (2012), 87–93.Google ScholarCross Ref
- Wiktoria Wilkowska, Julia Offermann-van Heek, Francisco Florez-Revuelta, and Martina Ziefle. 2021. Video Cameras for Lifelogging at Home: Preferred Visualization Modes, Acceptance, and Privacy Perceptions among German and Turkish Participants. Int. J. Hum.-Comput. Interact. 37, 15 (2021), 1436–1454.Google ScholarCross Ref
- Haiming Yin, Xiaodong Xu, and Lihua Ye. 2011. Big skin regions detection for adult image identification. In 2011 Workshop on Digital Media and Digital Content Management. IEEE, 242–247.Google ScholarDigital Library
- Salifu Yusif, Jeffrey Soar, and Abdul Hafeez-Baig. 2016. Older people, assistive technologies, and the barriers to adoption: A systematic review. Int. J. Med. Inform. 94(2016), 112–116.Google ScholarCross Ref
- Qing-Fang Zheng, Wei Zeng, Wei-Qiang Wang, and Wen Gao. 2006. Shape-based adult image detection. Int. J. of Image Graph. 6, 01 (2006), 115–124.Google ScholarCross Ref
- Shuai Zheng, Fan Yang, M Hadi Kiapour, and Robinson Piramuthu. 2018. Modanet: A large-scale street fashion dataset with polygon annotations. In Proceedings of the 26th ACM International Conference on Multimedia. 1670–1678.Google ScholarDigital Library
- Martina Ziefle, Simon Himmel, and Wiktoria Wilkowska. 2011. When your living space knows what you do: Acceptance of medical home monitoring by different technologies. In Symposium of the Austrian HCI and Usability Engineering Group. Springer, Berlin, Heidelberg, 607–624.Google ScholarDigital Library
- Haiqiang Zuo, Heng Fan, Erik Blasch, and Haibin Ling. 2017. Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24, 3 (2017), 289–293.Google ScholarCross Ref
Index Terms
- Underneath Your Clothes: A Social and Technological Perspective on Nudity in The Context of AAL Technology
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