Microsystem Technologies

, Volume 24, Issue 10, pp 4399–4413 | Cite as

Empirical study of the usability and interactivity of an augmented-reality dressing mirror

  • Chen-Chiou Chiu
  • Lai-Chung Lee
Technical Paper


In recent years, “selfies” have become popular around the world, and this has resulted in many makeup apps on Google’s Android and Apple’s iOS app stores. However, makeup apps can only detect users’ faces and then place makeup on part of the face or add some effects. In the Google/Apple stores, some apparel marketing companies have provided apps for consumers to give some advice on the products they can wear. However, there has yet to be an application that provides help with taking pictures and saving images in a library to show consumers’ dressing styles. This study designed an application to provide people with their dress styles, and we named it “Selfie Mirror”. We also conducted a questionnaire of users’ perception of functionality and interface for future revisions.


  1. Allahyari T, Sahraneshin Samani A, Khalkhali HR (2017) Validity of the Microsoft Kinect for measurement of neck angle: comparison with electrogoniometry. Int J Occup Saf Ergon 23(4):524–532. CrossRefGoogle Scholar
  2. Ballouki I, Douimi M, Ouzizi L (2017) Decision support tool for supply chain configuration considering new product re-design: an agent-based approach. J Adv Manuf Syst 16(4):291–315CrossRefGoogle Scholar
  3. Bauer A, Neog DR, Dicko AH, Paie DK, Faure F, Palombi O, Troccaz J (2017) Anatomical augmented reality with 3D commodity tracking and image-space alignment. Comput Graph (Pergamon) 69:140–153. CrossRefGoogle Scholar
  4. Chen Y, Yao J, Jin H, He C, Chen H (2017) Exploring the evolution of new mobile services. Sci Program 2017:5159690. CrossRefGoogle Scholar
  5. Chen CC, Chen PJ, Chen BT, Hsu CM (2018) Combination of micro and cloud-based system to ambulance. Microsyst Technol 24(1):165–177. CrossRefGoogle Scholar
  6. Cho NG, Park SH, Park JS, Park U, Lee SW (2017) Compositional interaction descriptor for human interaction recognition. Neurocomputing 267(6):169–181. CrossRefGoogle Scholar
  7. Costa N, Patrício L, Morelli N, Magee CL (2017) Bringing service design to manufacturing companies: integrating PSS and service design approaches. Des Stud. CrossRefGoogle Scholar
  8. Devi N, Easwarakumar KS (2017) A clinical evaluation of human computer interaction using multi modal fusion techniques. J Med Imaging Health Inf 7(8):1759–1766. CrossRefGoogle Scholar
  9. Du HB, Zhao YW, Han JD, Zhao XG, Wang Z, Song GL (2017) Data fusion of human skeleton joint tracking using two Kinect sensors and extended set membership filter. Zidonghua Xuebao/Acta Automatica Sinica 42(12):1886–1898. CrossRefGoogle Scholar
  10. Gunawan TS, Bahari B, Kartiwi M (2017) Development of educational game for primary school mathematics using microsoft kinect. Indones J Electr Eng Comput Sci 6(2):457–463. CrossRefGoogle Scholar
  11. Iriarte I, Alberdi A, Urrutia E, Justel D (2017) Beyond customer satisfaction. Supporting organisational change through service design. A case study in the insurance industry. Des J 20:S424–S434. CrossRefGoogle Scholar
  12. Kim Y (2017) Dance motion capture and composition using multiple RGB and depth sensors. Int J Distrib Sens Netw 13(2):1–11. CrossRefGoogle Scholar
  13. Kim Y, Baek S, Bae BC (2017) Motion capture of the human body using multiple depth sensors. ETRI J 39(2):181–190. CrossRefGoogle Scholar
  14. Klemm C, Pieters W (2017) Game mechanics and technological mediation: an ethical perspective on the effects of MMORPG’s. Ethics Inf Technol 19(2):81–93. CrossRefGoogle Scholar
  15. Lim KM, Tan AWC, Tan SC (2017) A four dukkha state-space model for hand tracking. Neurocomputing 267(6):311–319. CrossRefGoogle Scholar
  16. Manasrah AM, Smadi T, ALmomani A (2017) A variable service broker routing policy for data center selection in cloud analyst. J King Saud Univ Comput Inf Sci 29(3):365–377. CrossRefGoogle Scholar
  17. Mizoguchi S, Ishii T, Nemoto Y, Kaneda M, Bando A, Nakamura T, Shimomura Y (2017) A method for supporting customer model construction: using a topic model for public service design. Paper presented at the Serviceology for Smart Service System, TokyoGoogle Scholar
  18. Ogawa A, Mita A, Yorozu A, Takahashi M (2017) Markerless knee joint position measurement using depth data during stair walking. Sensors 17(11):2698. CrossRefGoogle Scholar
  19. Priporas C-V, Stylos N, Fotiadis AK (2017) Generation Z consumers’ expectations of interactions in smart retailing: a future agenda. Comput Hum Behav 77:374–381. CrossRefGoogle Scholar
  20. Samad R, Bakar MZA, Pebrianti D, Mustafa M, Abdullah NRH (2017) Elbow flexion and extension rehabilitation exercise system using marker-less kinect-based method. Int J Electr Comput Eng 7(3):1602–1610Google Scholar
  21. Thakur SS, Sing JK (2017) Real-time monitoring of vehicle’s movement and damage avoidance cum repair system using web service negotiation. Microsyst Technol 23(9):4279–4288. CrossRefGoogle Scholar
  22. Wang Y, Ma HS, Yang JH, Wang KS (2017) Industry 4.0: a way from mass customization to mass personalization production. Adv Manuf 5(4):311–320. CrossRefGoogle Scholar
  23. Williams EJ, Beardmore A, Joinson AN (2017) Individual differences in susceptibility to online influence: a theoretical review. Comput Hum Behav 72:412–421. CrossRefGoogle Scholar
  24. Yağanoğlu M, Köse C (2017) Wearable vibration based computer interaction and communication system for deaf. Appl Sci (Switzerland) 7(12):1296Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Taipei University of TechnologyTaipeiTaiwan, ROC

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