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Immersive Educational Recycling Assistant (ERA): Learning Waste Sorting in Augmented Reality

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Immersive Learning Research Network (iLRN 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1904))

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Abstract

For a sustainable living, it is everyone’s responsibility to do our best at recycling. However, waste classification can be complex. The existing resources may not have sufficient information or dynamic feedback to resolve our everyday garbage disposal. In this work, we design an interactive mobile Augmented Reality (AR) application, Educational Recycling Assistant (ERA), to educate people in doing sound day-to-day waste management. ERA utilizes dynamic object detection and provides in-situ guidance for proper garbage disposal. A user study was designed and conducted to investigate the effects and the user experiences. We found that the users achieved significantly higher garbage binning accuracy with the ERA app. The participants also improved their recycling and garbage disposal knowledge after using the app, particularly in complex items.

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Notes

  1. 1.

    https://github.com/garythung/trashnet.

  2. 2.

    https://developers.google.com/ar.

  3. 3.

    https://github.com/google-ar/sceneform-android-sdk.

References

  1. Deep learning-based waste detection in natural and urban environments. Waste Management 138, 274–284 (2022)

    Google Scholar 

  2. trashnet (2022). https://github.com/garythung/trashnet. Accessed 27 Sept 2022

  3. Abdallah, M., Talib, M.A., Feroz, S., Nasir, Q., Abdalla, H., Mahfood, B.: Artificial intelligence applications in solid waste management: a systematic research review. Waste Manage. 109, 231–246 (2020)

    Article  Google Scholar 

  4. Aishwarya, A., Wadhwa, P., Owais, O., Vashisht, V.: A waste management technique to detect and separate non-biodegradable waste using machine learning and YOLO algorithm. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE (2021)

    Google Scholar 

  5. Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., MacIntyre, B.: Recent advances in augmented reality. IEEE Comput. Graphics Appl. 21(6), 34–47 (2001)

    Article  Google Scholar 

  6. Bell, J., et al.: Informal stem education: resources for outreach, engagement and broader impacts. Sci. Educ. (CAISE), 1–28 (2016)

    Google Scholar 

  7. Boud, D., Garrick, J., Greenfield, K.: Understanding learning at work (2000)

    Google Scholar 

  8. Buragohain, A., Mali, B., Saha, S., Singh, P.K.: A deep transfer learning based approach to detect COVID-19 waste. Internet Technol. Lett. 5(3), e327 (2022)

    Article  Google Scholar 

  9. Chung, C.Y., Awad, N., Hsiao, I.H.: Collaborative programming problem-solving in augmented reality: multimodal analysis of effectiveness and group collaboration. Australas. J. Educ. Technol. 37(5), 17–31 (2021)

    Article  Google Scholar 

  10. Council, N.R., et al.: Learning Science in Informal Environments: People, Places, and Pursuits. National Academies Press, Washington (2009)

    Google Scholar 

  11. Dede, C.: Immersive interfaces for engagement and learning. Science 323(5910), 66–69 (2009)

    Article  Google Scholar 

  12. Dunleavy, M., Dede, C.: Augmented reality teaching and learning. In: Spector, J.M., Merrill, M.D., Elen, J., Bishop, M.J. (eds.) Handbook of Research on Educational Communications and Technology, pp. 735–745. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-3185-5_59

    Chapter  Google Scholar 

  13. Fulton, M.S., Hong, J., Sattar, J.: Trash-ICRA19: a bounding box labeled dataset of underwater trash (2020)

    Google Scholar 

  14. Hong, J., Fulton, M., Sattar, J.: Trashcan: a semantically-segmented dataset towards visual detection of marine debris. ar**v preprint ar**v:2007.08097 (2020)

  15. Ibáñez, M.B., Delgado-Kloos, C.: Augmented reality for stem learning: a systematic review. Comput. Educ. 123, 109–123 (2018)

    Article  Google Scholar 

  16. Johnson-Glenberg, M.C., Megowan-Romanowicz, C.: Embodied science and mixed reality: how gesture and motion capture affect physics education. Cogn. Research: Principles Implications 2(1), 1–28 (2017)

    Article  Google Scholar 

  17. Kaufmann, H., Dünser, A.: Summary of usability evaluations of an educational augmented reality application. In: Shumaker, R. (ed.) ICVR 2007. LNCS, vol. 4563, pp. 660–669. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73335-5_71

    Chapter  Google Scholar 

  18. Kolb, D.A.: Experiential Learning: Experience as the Source of Learning and Development. FT press, Upper Saddle River (2014)

    Google Scholar 

  19. Kraft, M., Piechocki, M., Ptak, B., Walas, K.: Autonomous, onboard vision-based trash and litter detection in low altitude aerial images collected by an unmanned aerial vehicle. Remote Sens. 13(5), 965 (2021)

    Article  Google Scholar 

  20. Kuznetsova, A., et al.: The open images dataset v4. Int. J. Comput. Vision 128(7), 1956–1981 (2020)

    Article  Google Scholar 

  21. Lin, W.: YOLO-green: a real-time classification and object detection model optimized for waste management. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 51–57. IEEE (2021)

    Google Scholar 

  22. Lu, Y., Yang, B., Gao, Y., Xu, Z.: An automatic sorting system for electronic components detached from waste printed circuit boards. Waste Manag. 137, 1–8 (2022)

    Article  Google Scholar 

  23. Mao, W.L., Chen, W.C., Wang, C.T., Lin, Y.H.: Recycling waste classification using optimized convolutional neural network. Resour. Conserv. Recycl. 164(105132), 105132 (2021)

    Article  Google Scholar 

  24. Narayan, Y.: Deepwaste: applying deep learning to waste classification for a sustainable planet. ar**v preprint ar**v:2101.05960 (2021)

  25. Padalkar, A.S.: An Object Detection and Scaling Model for Plastic Waste Sorting (Doctoral dissertation). Ph.D. thesis, Dublin, National College of Ireland (2021)

    Google Scholar 

  26. Proença, P.F., Simões, P.: Taco: trash annotations in context for litter detection. ar**v preprint ar**v:2003.06975 (2020)

  27. Rabano, S.L., Cabatuan, M.K., Sybingco, E., Dadios, E.P., Calilung, E.J.: Common garbage classification using MobileNet. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE (2018)

    Google Scholar 

  28. Ruiz, V., Sánchez, Á., Vélez, J.F., Raducanu, B.: Automatic image-based waste classification. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2019. LNCS, vol. 11487, pp. 422–431. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19651-6_41

    Chapter  Google Scholar 

  29. Sefton-Green, J.: Literature review in informal learning with technology outside school. A NESTA Futurelab Series (2004)

    Google Scholar 

  30. Silvertown, J.: A new dawn for citizen science. Trends in Ecol. Evol. 24(9), 467–471 (2009)

    Article  Google Scholar 

  31. Soni, U., Roy, A., Verma, A., Jain, V.: Forecasting municipal solid waste generation using artificial intelligence models–a case study in India. SN Appl. Sci. 1(2), 1–10 (2019)

    Article  Google Scholar 

  32. de Souza Melaré, A.V., González, S.M., Faceli, K., Casadei, V.: Technologies and decision support systems to aid solid-waste management: a systematic review. Waste Manage. 59, 567–584 (2017)

    Article  Google Scholar 

  33. Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

    Google Scholar 

  34. Tang, A., Owen, C., Biocca, F., Mou, W.: Comparative effectiveness of augmented reality in object assembly. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 73–80 (2003)

    Google Scholar 

  35. Vygotsky, L.: Interaction between learning and development. Read. Dev. Child. 23(3), 34–41 (1978)

    Google Scholar 

  36. Wahyutama, A.B., Hwang, M.: YOLO-based object detection for separate collection of recyclables and capacity monitoring of trash bins. Electron. (Basel) 11(9), 1323 (2022)

    Google Scholar 

  37. Wang, H., Li, Y., Dang, L.M., Ko, J., Han, D., Moon, H.: Smartphone-based bulky waste classification using convolutional neural networks. Multimed. Tools Appl. 79(39–40), 29411–29431 (2020)

    Article  Google Scholar 

  38. Wang, T., Cai, Y., Liang, L., Ye, D.: A multi-level approach to waste object segmentation. Sensors 20(14), 3816 (2020)

    Article  Google Scholar 

  39. Yang, M., Thung, G.: Classification of trash for recyclability status. CS229 Proj. Rep. 2016(1), 3 (2016)

    Google Scholar 

  40. Yuen, S.C.Y., Yaoyuneyong, G., Johnson, E.: Augmented reality: an overview and five directions for AR in education. J. Educ. Technol. Dev. Exch. (JETDE) 4(1), 11 (2011)

    Google Scholar 

  41. Zhang, Q., et al.: A multi-label waste detection model based on transfer learning. Resour. Conserv. Recycl. 181(106235), 106235 (2022)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Santa Clara University, Santa Clara, CA, 95053, USA

    Qiming Sun & I-Han Hsiao

  2. National Chengchi University, Taipei, Taiwan

    Shih-Yi Chien

Authors
  1. Qiming Sun
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  2. I-Han Hsiao
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  3. Shih-Yi Chien
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Corresponding author

Correspondence to I-Han Hsiao .

Editor information

Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Marie-Luce Bourguet

  2. University of Potsdam, Potsdam, Germany

    Jule M. Krüger

  3. University of Aveiro, Aveiro, Portugal

    Daniela Pedrosa

  4. Goethe University Frankfurt, Frankfurt am Main, Germany

    Andreas Dengel

  5. British Telecom Research Labs, Ipswich, UK

    Anasol Peña-Rios

  6. University of Montana, Missoula, MT, USA

    Jonathon Richter

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Sun, Q., Hsiao, IH., Chien, SY. (2024). Immersive Educational Recycling Assistant (ERA): Learning Waste Sorting in Augmented Reality. In: Bourguet, ML., Krüger, J.M., Pedrosa, D., Dengel, A., Peña-Rios, A., Richter, J. (eds) Immersive Learning Research Network. iLRN 2023. Communications in Computer and Information Science, vol 1904. Springer, Cham. https://doi.org/10.1007/978-3-031-47328-9_34

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  • DOI: https://doi.org/10.1007/978-3-031-47328-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47327-2

  • Online ISBN: 978-3-031-47328-9

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