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Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer Sensor

Published: 24 September 2021 Publication History

Abstract

As the number of patients in hospitals is increasing day by day, proper monitoring and hospital care towards patients must be ensured. In this regard, Nurse care activity recognition can play a significant role in improving the existing healthcare system. Research in this domain is very challenging because nursing activities are very complex and troublesome than other normal activities. Nursing activities are dependent not only on nurses but also on the patients’ various states of illness. As a result, each activity has a high intra-class variation. We have participated in ’3rd Nurse Care Activity Recognition Challenge 2021’ and proposed a simple machine learning approach to recognize nursing activities. After data pre-processing and feature engineering, we have used several machine learning algorithms. Among them, we have achieved our best results in the Random Forest model. Using this model, We have obtained 72 percent validation accuracy classifying several challenging activities.

References

[1]
[1] Sayeda Shamma Alia, Kohei Adachi, Nhat Tan Le, Haru Kaneko, Paula Lago, Sozo Inoue, April 29, 2021, ”Third Nurse Care Activity Recognition Challenge”, IEEE Dataport. 2021.
[2]
[2]Xi’ang Li, Jinqi Luo, and Rabih Younes. 2020. ActivityGAN: generative adversarial networks for data augmentation in sensor-based human activity recognition. In 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(UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 249–254.
[3]
[3] Wang, Z.; Yang, Z.; Dong, T. A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time. Sensors 2017, 17, 341. https://doi.org/10.3390/s17020341.
[4]
[4]Tun, S.Y.Y., Madanian, S. & Mirza, F. Internet of things (IoT) applications for elderly care: a reflective review. Aging Clin Exp Res 33, 855–867 (2021). https://doi.org/10.1007/s40520-020-01545-9.
[5]
[5]Md Sadman Siraj, Md Ahasan Atick Faisal, Omar Shahid, Farhan Fuad Abir, Tahera Hossain, Sozo Inoue, and Md Atiqur Rahman Ahad. 2020. UPIC: user and position independent classical approach for locomotion and transportation modes recognition. In 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 (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 340–345.
[6]
[6]Chan Naseeb and Bilal Al Saeedi. 2020. Activity recognition for locomotion and transportation dataset using deep learning. In 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 (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 329–334.
[7]
[7]A. Krause, A. Smailagic and D. P. Siewiorek, ”Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array,” in IEEE Transactions on Mobile Computing, vol. 5, no. 2, pp. 113-127, Feb. 2006.
[8]
[8]Dahmen, J.; Thomas, B.L.; Cook, D.J.; Wang, X. Activity Learning as a Foundation for Security Monitoring in Smart Homes. Sensors 2017, 17, 737. https://doi.org/10.3390/s17040737
[9]
[9]Simon Keizer, Mary Ellen Foster, Zhuoran Wang, and Oliver Lemon. 2014. Machine Learning for Social Multiparty Human–Robot Interaction. ACM Trans. Interact. Intell. Syst. 4, 3, Article 14 (October 2014), 32 pages.
[10]
[10]Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, https://doi.org/10.5194/hess-14-1881-2010, 2010.
[11]
[11]Inoue, Sozo & Ueda, Naonori & Nohara, Yasunobu & Nakashima, Naoki. (2016). Recognizing and Understanding Nursing Activities for a Whole Day with a Big Dataset. Journal of Information Processing. 24. 853-866. 10.2197/ipsjjip.24.853.
[12]
[12] Espinilla, Macarena, Javier Medina, and Chris Nugent. 2018. ”UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living” Proceedings 2, no. 19: 1267. https://doi.org/10.3390/proceedings2191267
[13]
[13] Akram Bayat, Marc Pomplun, Duc A. Tran, A Study on Human Activity Recognition Using Accelerometer Data from Smartphones, Procedia Computer Science, Volume 34, 2014, Pages 450-457, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2014.07.009.
[14]
[14]M. Z. Uddin and M. M. Hassan, ”Activity Recognition for Cognitive Assistance Using Body Sensors Data and Deep Convolutional Neural Network,” in IEEE Sensors Journal, vol. 19, no. 19, pp. 8413-8419, 1 Oct.1, 2019.
[15]
[15]C. Dewi and R. Chen, ”Human Activity Recognition Based on Evolution of Features Selection and Random Forest,” 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, pp. 2496-2501.
[16]
[16]F. Rustam et al., ”Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model,” in IEEE Access, vol. 8, pp. 218898-218910, 2020.
[17]
[17]Mekruksavanich, S.; Jitpattanakul, A. LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors 2021, 21, 1636. https://doi.org/10.3390/s21051636.
[18]
[18]A. Das Antar, M. Ahmed and M. A. R. Ahad, ”Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review,” 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2019, pp. 134-139.
[19]
[19]2019. Nurse Care Activity Recognition Challenge. https://doi.org/10.21227/2cvj-bs21
[20]
[20]Second Nurse Care Activity Recognition Challenge: https://abcresearch.github.io/nurse2020/. Accessed: 2021-03-17.
[21]
[21]Sozo Inoue, Paula Lago, Tahera Hossain, Tittaya Mairittha, and Nattaya Mairittha. 2019. Integrating Activity Recognition and Nursing Care Records: The System, Deployment, and a Verification Study. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 86 (September 2019), 24 pages.
[22]
[22]Md. Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, and Mohammad Shoyaib. 2019. Can a simple approach identify complex nurse care activity? In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 736–740.
[23]
[23]Xin Cao, Wataru Kudo, Chihiro Ito, Masaki Shuzo, and Eisaku Maeda. 2019. Activity recognition using ST-GCN with 3D motion data. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 689–692.
[24]
[24]Irbaz, Sabik & Azad, Abir & Sathi, Tanjila Alam & Lota, Lutfun. (2020). Nurse Care Activity Recognition Based on Machine Learning Techniques Using Accelerometer Data. 10.1145/3410530.3414339.
[25]
[25]Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. H. M. Nazmus Sakib, Sriman Bidhan Baray, and M. A. R. Ahad. 2020. Complex nurse care activity recognition using statistical features. In 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 (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 384–389.
[26]
[26]Sayeda Shamma Alia, Kohei Adachi, Tahera Hossain, Nhat Tan Le, Haru Kaneko, Paula Lago, Tsuyoshi Okita, Sozo Inoue, Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data? In Proceedings of the 2021 ACM International Joint Conference and 2021 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers Adjunct, 2021.

Cited By

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  • (2023)Leveraging a Smartwatch for Activity Recognition in SalatIEEE Access10.1109/ACCESS.2023.331126111(97284-97317)Online publication date: 2023
  • (2021)Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning MethodsInternational Journal of Innovations in Science and Technology10.33411/IJIST/20210304053:4(186-197)Online publication date: 28-Dec-2021

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cover image ACM Conferences
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
September 2021
711 pages
ISBN:9781450384612
DOI:10.1145/3460418
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]

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Publication History

Published: 24 September 2021

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Author Tags

  1. Accelerometer Data
  2. Class imbalance
  3. Machine learning
  4. Nurse care activity Recognition
  5. Random Forest
  6. Smartphone

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2023)Leveraging a Smartwatch for Activity Recognition in SalatIEEE Access10.1109/ACCESS.2023.331126111(97284-97317)Online publication date: 2023
  • (2021)Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning MethodsInternational Journal of Innovations in Science and Technology10.33411/IJIST/20210304053:4(186-197)Online publication date: 28-Dec-2021

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