Skip to main content
SpringerLink
Log in

Bento Packaging Activity Recognition Based on Statistical Features

  • Conference paper
  • First Online:
Sensor- and Video-Based Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 291))

  • 205 Accesses

Abstract

Due to the fast advancements of low-cost micro-embedded sensors and MoCap sensors, human action recognition has become an essential study topic and is garnering a lot of interest in different sectors. Recently, it is drawing a lot of attention in human-robot collaboration to assist human to preform regular tasks step-wise because it is difficult to obtain human labor at a lower wage to monitor industrial works. In this work, we have presented a straightforward machine learning paradigm to recognize ten different Bento (lunch-box) packaging activities in real-time world. Unlike other skeleton-based human activity recognition domain, it is a very challenging task due to the absence of lower-body marker information. Under these circumstances, we have provided an in-depth statistical analysis of different Bento packaging activities. After feature extraction process, we have used several machine algorithms and obtained best results in random forest classifier using hyperparameter tuning. We have achieved 64.9% validation accuracy using leave-one-out method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahad, M.A.R.: Vision and sensor-based human activity recognition: challenges ahead. https://doi.org/10.4018/978-1-7998-2584-5.ch002 (2020)

  2. Ahad, M.A.R., Antar, A.D., Shahid, O.: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1–11 (2019)

    Google Scholar 

  3. Abbate, S., Avvenuti, M., Corsini, P., Light, J., Vecchio, A.: Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey, wireless sensor networks: application—centric Design, Geoff V Merrett and Yen Kheng Tan, IntechOpen, https://doi.org/10.5772/13802 (2010)

  4. Liao, Y., Vakanski, A., Xian, M., Paul, D., Baker, R.: A review of computational approaches for evaluation of rehabilitation exercises, Comput. Biol. Med. 119 (2020)

    Google Scholar 

  5. Ho, I.K., Goldschneider, K.R., Kashikar-Zuck, S., Kotagal, U., Tessman, C., Jones, B.: Healthcare utilization and indirect burden among families of pediatric patients with chronic pain. J. Musculoskeletal Pain 16(3), 155–164 (2008)

    Article  Google Scholar 

  6. Machlin, S.R., Chevan, J., Yu, W.W., Zodet, M.W.: Determinants of utilization and expenditures for episodes of ambulatory physical therapy among adults. Phys. Therapy 91(7), 1018–1029 (2011)

    Article  Google Scholar 

  7. Abidine, B.M., Fergani, L., Fergani, B., Oussalah., M.: The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition. Pattern Anal. Appl. 21(1), 119–138 (2018)

    Google Scholar 

  8. Hu, C., Chen, Y., Hu, L., Peng, X.: A novel random forests based class incremental learning method for activity recognition. Pattern Recognition 78, 277–290 (2018)

    Article  Google Scholar 

  9. Förster, K., Monteleone, S., Calatroni, A., Roggen, D., Troster, G.: Incremental kNN classifier exploiting correct-error teacher for activity recognition. In: Ninth International Conference on Machine Learning and Applications 2010, 445–450 (2010)

    Google Scholar 

  10. Xiao, Q., Song, R.: Action recognition based on hierarchical dynamic Bayesian network. Multimed Tools Appl. 77, 6955–6968 (2018)

    Article  Google Scholar 

  11. Sok, P., Xiao, T., Azeze, Y., Jayaraman, A., Albert, M.V.: Activity recognition for incomplete spinal cord injury subjects using hidden Markov models. IEEE Senso. J. 18(15), 6369–6374 (2018)

    Article  Google Scholar 

  12. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Google Scholar 

  13. Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 no. 1 2016)

    Google Scholar 

  14. Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks. IEEE Inter. Conf. Comput. Vis. (ICCV) 2017, 1012–1020 (2017)

    Google Scholar 

  15. Wang, L., Gjoreskia, H., Murao, K., Okita, T., Roggen, D.: Summary of the sussex-huawei locomotion-transportation recognition challenge. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp’18), 1521–1530 (2018)

    Google Scholar 

  16. Lago, P., Alia, S.S., Takeda, S., Mairittha, T., Mairittha, N., Faiz, F., Nishimura, Y., Adachi, K., Okita, T., Charpillet, F., Inoue, S.: Nurse care activity recognition challenge: summary and results. UbiComp/ISWC’19 Adjunct, pp. 746–751 (2019)

    Google Scholar 

  17. Alia, S.S., Lago, P., Adachi, K., Hossain, T., Goto, H., Okita, T., Inoue, S.: Summary of the 2nd nurse care activity recognition challenge using lab and field data. In: Adjunct Proc. UbiComp-ISWC’20, pp. 378–383 (2020)

    Google Scholar 

  18. Pinilla, M., Medina, J., Nugent, C.: UCAmI Cup. Analyzing the UJA human activity recognition dataset of activities of daily living. Proceedings 2, 19, 1267

    Google Scholar 

  19. Alia, S.S., et al.: Summary of the cooking activity recognition challenge. In: Ahad M.A.R., Lago P., Inoue S. (eds) Human activity recognition challenge. Smart innovation, systems and technologies, vol. 199. Springer (2021)

    Google Scholar 

  20. Alia, S.S., Adachi, K., Nahid, N., Kaneko, H., Lago, P., Inoue, S.: Bento packaging activity recognition challenge. IEEE Dataport. (2021). https://dx.doi.org/10.21227/cwhs-t440.s

  21. Chaudhry, R., Ofli, F., Kurillo, G., Bajcsy, R., Vidal, R.: Bio-inspired dynamic 3D discriminative skeletal features for human action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, vol. 2013, pp. 471–478 (2013)

    Google Scholar 

  22. Ahad, M.A.R., Ahmed, M., Antar, A.D., Makihara, Y., Yagi, Y.: Action recognition using kinematics posture feature on 3D skeleton joint locations. Pattern Recogn. Lett. 145, 216–224 (2021)

    Article  Google Scholar 

  23. Adachi, K., Alia, S.S., Nahid, N., Kaneko, H., Lago, P., Inoue, S.: Summary of the bento Packaging activity recognition challenge. In: The 3rd International Conference on Activity and Behavior Computing (ABC2021) (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Dhaka, Dhaka, Bangladesh

    Faizul Rakib Sayem & Md. Mamun Sheikh

  2. University of East London, London, UK

    Md Atiqur Rahman Ahad

Authors
  1. Faizul Rakib Sayem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Mamun Sheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md Atiqur Rahman Ahad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md Atiqur Rahman Ahad .

Editor information

Editors and Affiliations

  1. University of East London, London, UK

    Md Atiqur Rahman Ahad

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Sozo Inoue

  3. Sussex University, Brighton, UK

    Daniel Roggen

  4. Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan

    Kaori Fujinami

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rakib Sayem, F., Sheikh, M.M., Ahad, M.A.R. (2022). Bento Packaging Activity Recognition Based on Statistical Features. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_13

Download citation

Publish with us

Policies and ethics

Search

Navigation