skip to main content
10.1145/3410530.3414611acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Summary of the 2nd nurse care activity recognition challenge using lab and field data

Authors Info & Claims
Published:12 September 2020Publication History

ABSTRACT

2nd Nurse Care Activity Recognition Challenge using Lab and Field Data is organized as a part of HASCA workshop and is continuation of Nurse Care Activity Recognition Challenge [7]. We give the description of the dataset and summarize the approaches used by the teams in this Challenge. In this challenge, data collected in both lab and real-world setting is provided to the challenge participants with an aim to bridge the gap between lab and practical field to reduce the workload of the nurses. The challenge was started on May 1, 2020 and continued until July 9, 2020. Accuracy is used as performance metric to evaluate the submissions. The winning team used k-NN classifier and achieved about 22.35% accuracy.

References

  1. Sayeda Shamma Alia, Paula Lago, Shingo Takeda, Kohei Adachi, Brahim Benaissa, M.A.R. Ahad, and Sozo Inoue. 2020. Summary of the Cooking Activity Recognition Challenge. In Human Activity Recognition Challenge. Springer.Google ScholarGoogle Scholar
  2. Motion Capture Company. 2019. http://motionanalysis.com/movement-analysis/. [Online].Google ScholarGoogle Scholar
  3. Yiwen Dong, Jingxiao Liu, Yitao Gao, Sulagna Sarkar, Zhizhang Hu, Jonathon Fagert, Shijia Pan, Pei Zhang, Hae Young Noh, and Mostafa Mirshekari. 2020. A Hierarchical Sequence-to-One Approach for Nurse Care Activity Recognition Using Vibration-BasedWearable Sensor. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  4. Md. Ahasan Atick Faisal, Md. Sadman Siraj, Md. Tahmeed Abdullah, Omar Shahid, Farhan Fuad Abir, and M. A. R. Ahad. 2020. Classical Machine Learning Approach to Classify Complex Nurse Activity. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  5. Sozo Inoue, Sayeda Shamma Alia, Paula Lago, Hiroki Goto, and Shingo Takeda. 2020. NURSE CARE ACTIVITIES DATASETS: IN LABORATORY AND IN REAL FIELD. Google ScholarGoogle ScholarCross RefCross Ref
  6. Mohammad Sabik Irbaz, Abir Azad, Tanjila Alam Sathi, and Lutfun Nahar Lota. 2020. Nurse Care Activity Recognition based on Machine Learning and Feature Extraction for Accelerometer Data. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  7. Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, François CHARPILLET, and Sozo Inoue. 2019. Nurse care activity recognition challenge: summary and results. In UbiComp/ISWC '19 Adjunct: ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Carolin Lübbe, Björn Friedrich, Sebastian Fudickar, Sandra Hellmers, and Andreas Hein. 2020. Feature Based Random Forest Nurse Care Activity Recognition using Accelerometer Data. In Proc. HASCA 2020. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, and Nobuo Kawaguchi. 2020. Nurse Care Activity Recognition Challenge: An Approach with Data Uniforming Pre-processing. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  10. World Health Organization. 2019. Global Spending on Health: A World in Transition. https://www.who.int/health_financing/documents/health-expenditure-report-2019.pdf [Online].Google ScholarGoogle Scholar
  11. Families Parkinson's Disease Economic Burden on Patients and Doubling Previous Estimates the Federal Government Is $52 Billion. 2019. https://www.michaeljfox.org/publication/parkinsons-disease-economic-burden-patients-families-and-federal-government-52-billion [Online].Google ScholarGoogle Scholar
  12. Arafat Rahman, Nazmun Nahid, Iqbal Hassan, and M. A. R. Ahad. 2020. Nurse Care Activity Recognition: Using Random Forest to Handle Imbalanced Class Problem. In Proc. HASCA 2020. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Md. Golam Rasul, Mashrur Hossain Khan, and Lutfun Nahar Lota. 2020. Nurse-Care Activity Recognition based on Convolution Neural Network for Accelerometer Data. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  14. Hannah Ritchie and Max Roser. 2019. Age Structure. https://ourworldindata.org/age-structureGoogle ScholarGoogle Scholar
  15. Malisha Islam Tapotee, Shahamat Mustavi Tasin, Promit Basak, MD Mamun Sheikh, A.H.M. Nazmus Sakib, and Sriman Bidhan Baray. 2020. Simple Machine Learning Techniques to Identify Complex Nurse Care Activity. In Proc. HASCA 2020. ACM.Google ScholarGoogle Scholar
  16. Smart Lifecare Society Creation Unit. 2019. http://https://smartlife.care/. [Online; accessed 19-December-2019].Google ScholarGoogle Scholar
  17. Bradley University. 2018. What does the future of the nursing industry look like? https://onlinedegrees.bradley.edu/blog/what-does-the-future-of-the-nursing-industry-look-like/ [Online].Google ScholarGoogle Scholar

Index Terms

  1. Summary of the 2nd nurse care activity recognition challenge using lab and field data

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
      September 2020
      732 pages
      ISBN:9781450380768
      DOI:10.1145/3410530

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 September 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate764of2,912submissions,26%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader