Conference Paper2019Cited by 26

Can a simple approach identify complex nurse care activity?

Md Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, Mohammad Shoyaib

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

ACM, pp. 736–740

  • Good Health and Well-being — Relevance: 60%
DOI: 10.1145/3341162.3344859URL

Abstract

For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple human activities, e.g., walking, eating, and running. However, many of our daily life activities are usually more complex than those. To instigate research in complex activity recognition, the "Nurse Care Activity Recognition Challenge" [1] is initiated where six nurse activities are to be identified based on location, air pressure, motion capture, and accelerometer data. Our team, "IITDU", investigates the use of simple methods for this purpose. We first extract features from the sensor data and use one of the simplest classifiers, namely K-Nearest Neighbors (KNN). Experiment using an ensemble of KNN classifiers demonstrates that it is possible to achieve approximately 87% accuracy on 10-fold cross-validation and 66% accuracy on leave-one-subject-out cross-validation.

Keywords

  • k-nearest neighbors
  • Motion capture
  • ensemble classifiers
  • human activity recognition
  • air pressure sensing
  • Accelerometer
  • wearable sensors

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

References

  1. 1.Leo Breiman. (2001). Random Forests. Machine Learning, 45(1), 5–32[10.1023/a:1010933404324]
  2. 2.J.R. Quinlan. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106[10.1023/a:1022643204877]
  3. 3.Evelyn Fix, J. L. Hodges. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review, 57(3), 238[10.2307/1403797]
  4. 4.Songyang Zhang, Yang Yang, Jun Xiao, Xiaoming Liu, Yi Yang, Di Xie, Yueting Zhuang. (2018). Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks. IEEE Transactions on Multimedia, 20(9), 2330–2343[10.1109/tmm.2018.2802648]
  5. 5.Sadia Sharmin, Mohammad Shoyaib, Amin Ahsan Ali, Muhammad Asif Hossain Khan, Oksam Chae. (2019). Simultaneous feature selection and discretization based on mutual information. Pattern Recognition, 91, 162–174[10.1016/j.patcog.2019.02.016]
  6. 6.Diogo Carbonera Luvizon, Hedi Tabia, David Picard. (2017). Learning features combination for human action recognition from skeleton sequences. Pattern Recognition Letters, 99, 13–20[10.1016/j.patrec.2017.02.001]
  7. 7.Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, François Charpillet, Sozo Inoue. (2019). Nurse care activity recognition challenge. , 746–751[10.1145/3341162.3345577]
  8. 8.Benyue Su, Huang Wu, Min Sheng, Chuansheng Shen. (2019). Accurate Hierarchical Human Actions Recognition From Kinect Skeleton Data. IEEE Access, 7, 52532–52541[10.1109/access.2019.2911705]
  9. 9.Xiaoqiang Li, Yi Zhang, Dong Liao. (2017). Mining Key Skeleton Poses with Latent SVM for Action Recognition. Applied Computational Intelligence and Soft Computing, 2017, 1–11[10.1155/2017/5861435]
  10. 10.Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib. (2019). Inter-node Hellinger Distance based Decision Tree. , 1967–1973[10.24963/ijcai.2019/272]
  11. 11.Sozo INOUE, Naonori UEDA, Yasunobu NOHARA, Naoki NAKASHIMA. (2016). Understanding Nursing Activities with Long-term Mobile Activity Recognition with Big Dataset. Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, 2016(0), 1–11[10.5687/sss.2016.1]

Cited By

  1. 1.Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, François Charpillet, Sozo Inoue. (2019). Nurse care activity recognition challenge. , 746–751[10.1145/3341162.3345577]
  2. 2.Momal Ijaz, Renato Diaz, Chen Chen. (2022). Multimodal Transformer for Nursing Activity Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2064–2073[10.1109/cvprw56347.2022.00224]
  3. 3.Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. H. M. Nazmus Sakib, Sriman Bidhan Baray, M. A. R. Ahad. (2020). Complex nurse care activity recognition using statistical features. , 384–389[10.1145/3410530.3414338]
  4. 4.Md. Ahasan Atick Faisal, Md. Sadman Siraj, Md. Tahmeed Abdullah, Omar Shahid, Farhan Fuad Abir, M. A. R. Ahad. (2020). A pragmatic signal processing approach for nurse care activity recognition using classical machine learning. , 396–401[10.1145/3410530.3414337]
  5. 5.Yiwen Dong, Jingxiao Liu, Yitao Gao, Sulagna Sarkar, Zhizhang Hu, Jonathon Fagert, Shijia Pan, Pei Zhang, Hae Young Noh, Mostafa Mirshekari. (2020). A window-based sequence-to-one approach with dynamic voting for nurse care activity recognition using acceleration-based wearable sensor. , 390–395[10.1145/3410530.3414336]
  6. 6.Arafat Rahman, Iqbal Hassan, Md Atiqur Rahman Ahad. (2021). Nurse Care Activity Recognition: A Cost-Sensitive Ensemble Approach to Handle Imbalanced Class Problem in the Wild. , 440–445[10.1145/3460418.3479389]
  7. 7.Carolin Lübbe, Björn Friedrich, Sebastian Fudickar, Sandra Hellmers, Andreas Hein. (2020). Feature based random forest nurse care activity recognition using accelerometer data. , 408–413[10.1145/3410530.3414340]
  8. 8.Arafat Rahman, Nazmun Nahid, Björn Schuller, Md Atiqur Rahman Ahad. (2024). A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification. Scientific Reports, 14(1), 31667[10.1038/s41598-024-81228-x]
  9. 9.Md. Golam Rasul, Mashrur Hossain Khan, Lutfun Nahar Lota. (2020). Nurse care activity recognition based on convolution neural network for accelerometer data. , 425–430[10.1145/3410530.3414335]
  10. 10.Hamza Bayd, Patrice Guyot, Benoit Bardy, Pierre R. L. Slangen. (2022). Automatic Scoring of Synchronization from Fingers Motion Capture and Music Beats. Lecture notes in computer science, 235–245[10.1007/978-3-031-13321-3_21]
  11. 11.Faizul Rakib Sayem, MD Mamun Sheikh, Md Atiqur Rahman Ahad. (2021). Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer Sensor. , 446–451[10.1145/3460418.3479388]
  12. 12.Mohammad Sabik Irbaz, Abir Azad, Tanjila Alam Sathi, Lutfun Nahar Lota. (2020). Nurse care activity recognition based on machine learning techniques using accelerometer data. , 402–407[10.1145/3410530.3414339]
  13. 13.Sayeda Shamma Alia, Paula Lago, Sozo Inoue. (2020). MCoMat. , 232–237[10.1145/3410530.3414364]
  14. 14.Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, Nobuo Kawaguchi. (2020). Nurse care activity recognition challenge. , 414–418[10.1145/3410530.3414333]
  15. 15.Suravi Akhter, Sumon Ahmed, Ahmedul Kabir, Mohammad Shoyaib. (2022). A Relief Based Feature Subset Selection Method. Dhaka University Journal of Applied Science and Engineering, 6(2), 7–13[10.3329/dujase.v6i2.59214]
  16. 16.Md. Hasan Tarek, Md. Eusha Kadir, Mahir Mahbub, Pritom Saha Akash, Amin Ahsan Ali, Mohammad Shoyaib. (2020). Mutual Information based Feature Selection for Nurse Care Activity Recognition. , 1–6[10.1109/icievicivpr48672.2020.9306645]
  17. 17.Zubair Rahman Tusar, Maksuda Islam, Sadia Sharmin. (2021). Accelerometer based Complex Nurse Care Activity Recognition using Machine Learning Approach. , 452–457[10.1145/3460418.3479390]
  18. 18.Md. Mamun Sheikh, Faizul Rakib Sayem, Md Atiqur Rahman Ahad. (2021). A Residual Network with Focal Loss to Handle Class-imbalance Problem on Nurse Care Activity Recognition. , 1–8[10.1109/icievicivpr52578.2021.9564165]
  19. 19.Yasuyuki Yoshida, Hiroyasu Miwa, Satoshi Nakae, Satoki Ogiso, Ryosuke Ichikari, Takashi Okuma. (2025). Datasets, Machine Learning Recognition Performance, and Key Issues in Nursing Care Activity Recognition: A Systematic Review. IEEE Access, 13, 47570–47586[10.1109/access.2025.3550579]
  20. 20.M. Ashikuzzaman Kowshik, Yeasin Arafat Pritom, Md.Sohanur Rahman, Ali Akbar, Md Atiqur Rahman Ahad. (2021). Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features. , 434–439[10.1145/3460418.3479387]
  21. 21.Ryoichi Kojima, Roberto Legaspi, Kiyohito Yoshihara, Shinya Wada. (2020). Let’s Not Make It Complicated—Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset. Smart innovation, systems and technologies, 27–37[10.1007/978-981-15-8269-1_3]
  22. 22.Shadid Yousuf, Fabliha Noshin. (2024). Predicting Endotracheal Suctioning Activities Based On Skeletal Coordinates: A Novel Approach Combining Handcrafted Feature Engineering and Ensemble Learning. , 01–05[10.1109/spicscon64195.2024.10941319]
  23. 23.Lingyu Li, Haijing Han, Jupo Ma. (2025). Recognizing Nurse Care Activity. CIN Computers Informatics Nursing, 43(11)[10.1097/cin.0000000000001356]
  24. 24.Yeasin Arafat Pritom, Md. Sohanur Rahman, Hasib Ryan Rahman, M. Ashikuzzaman Kowshik, Md Atiqur Rahman Ahad. (2022). Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features. Smart innovation, systems and technologies, 193–205[10.1007/978-981-19-0361-8_12]
  25. 25.Xinyue Li, Jiaqing Liu, Yu Wang, Jien Kato. (2024). Spatio-Temporal Transformer with Hypergraph in Nursing Activity Recognition. , 2024, 1–4[10.1109/embc53108.2024.10781545]
  26. 26.Adrita Anwar, Malisha Islam Tapotee, Purnata Saha, Md Atiqur Rahman Ahad. (2022). Identification of Food Packaging Activity Using MoCap Sensor Data. Smart innovation, systems and technologies, 181–191[10.1007/978-981-19-0361-8_11]
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