Conference Paper2020Cited by 3

Mutual Information based feature selection for nurse care activity recognition

Md Hasan Tarek, Md Eusha Kadir, Mahir Mahbub, Pritom Saha Akash, Amin Ahsan Ali, Mohammad Shoyaib

2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR)

IEEE, pp. 1–6

  • Good Health and Well-being — Relevance: 71%
DOI: 10.1109/icievicivpr48672.2020.9306645URL

Abstract

Human activity recognition is a challenging task as performing the activities varies from person to person. For the last few years, many complex methods have been proposed to identify human activities from sensor readings. To date, several studies have been conducted successfully to identify simple activities and many commercial applications have also been developed. However, reliable recognition of complex activities is still an active research area. The nurse care activity dataset can be treated as a complex activity recognition dataset. Researchers have proposed many solutions to identify nurse care activity by extracting numerous handcrafted features or using the Spatio-temporal graph convolution method. However, some of these features may be noisy, redundant, or even distract the classifier performance. In this paper, we propose a feature selection strategy to select important features from the handcrafted features. We claim that the simple classifier can provide satisfactory performance once the important features are selected and the noisy ones are eliminated. Experiments demonstrate that our proposed approach achieves 87.93% accuracy and 87.97% f1-score in test data. This is a significant improvement over state-of-the-art approaches on this benchmark dataset and thereby establishing our claim.

Keywords

  • Feature selection
  • handcrafted features
  • human activity recognition
  • Mutual information
  • complex activities
  • sensor data

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.Ling Bao, Stephen S. Intille. (2004). Activity Recognition from User-Annotated Acceleration Data. Lecture notes in computer science, 1–17[10.1007/978-3-540-24646-6_1]
  2. 2.A.F. Bobick, J.W. Davis. (2001). The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3), 257–267[10.1109/34.910878]
  3. 3.Oscar D. Lara, Miguel A. Labrador. (2012). A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorials, 15(3), 1192–1209[10.1109/surv.2012.110112.00192]
  4. 4.Lu Xia, Chia-Chih Chen, J. K. Aggarwal. (2012). View invariant human action recognition using histograms of 3D joints. , 20–27[10.1109/cvprw.2012.6239233]
  5. 5.Emmanuel Munguia Tapia, Stephen S. Intille, Kent Larson. (2004). Activity Recognition in the Home Using Simple and Ubiquitous Sensors. Lecture notes in computer science, 158–175[10.1007/978-3-540-24646-6_10]
  6. 6.Charissa Ann Ronao, Sung-Bae Cho. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244[10.1016/j.eswa.2016.04.032]
  7. 7.Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz. (2012). Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine. Lecture notes in computer science, 216–223[10.1007/978-3-642-35395-6_30]
  8. 8.Tim van Kasteren, Athanasios Noulas, Gwenn Englebienne, Ben Kröse. (2008). Accurate activity recognition in a home setting. , 1–9[10.1145/1409635.1409637]
  9. 9.Michalis Vrigkas, Christophoros Nikou, Ioannis A. Kakadiaris. (2015). A Review of Human Activity Recognition Methods. Frontiers in Robotics and AI, 2[10.3389/frobt.2015.00028]
  10. 10.M. S. Ryoo. (2011). Human activity prediction: Early recognition of ongoing activities from streaming videos. , 1036–1043[10.1109/iccv.2011.6126349]
  11. 11.Akram Bayat, Marc Pomplun, Duc A. Tran. (2014). A Study on Human Activity Recognition Using Accelerometer Data from Smartphones. Procedia Computer Science, 34, 450–457[10.1016/j.procs.2014.07.009]
  12. 12.J.W. Davis, A.F. Bobick. (2002). The representation and recognition of human movement using temporal templates. , 928–934[10.1109/cvpr.1997.609439]
  13. 13.Douglas L. Vail, Manuela M. Veloso, John D. Lafferty. (2007). Conditional random fields for activity recognition. , 1–8[10.1145/1329125.1329409]
  14. 14.Beth Logan, Jennifer Healey, Matthai Philipose, Emmanuel Munguia, Stephen Intille. (2007). A Long-Term Evaluation of Sensing Modalities for Activity Recognition.
  15. 15.Stefan Dernbach, Barnan Das, Narayanan C. Krishnan, Brian L. Thomas, Diane J. Cook. (2012). Simple and Complex Activity Recognition through Smart Phones. , 214–221[10.1109/ie.2012.39]
  16. 16.Zhe Lin, Zhuolin Jiang, Larry S. Davis. (2009). Recognizing actions by shape-motion prototype trees. , 444–451[10.1109/iccv.2009.5459184]
  17. 17.Beth Logan, Jennifer Healey, Matthai Philipose, Emmanuel Munguia Tapia, Stephen Intille. (2007). A Long-Term Evaluation of Sensing Modalities for Activity Recognition. Lecture notes in computer science, 483–500[10.1007/978-3-540-74853-3_28]
  18. 18.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]
  19. 19.Rizwan Chaudhry, Ferda Ofli, Gregorij Kurillo, Ruzena Bajcsy, Rene Vidal. (2013). Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition. , 471–478[10.1109/cvprw.2013.153]
  20. 20.Tao Gu, Liang Wang, Zhanqing Wu, Xianping Tao, Jian Lu. (2010). A Pattern Mining Approach to Sensor-Based Human Activity Recognition. IEEE Transactions on Knowledge and Data Engineering, 23(9), 1359–1372[10.1109/tkde.2010.184]
  21. 21.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]
  22. 22.Kaustubh Kulkarni, Georgios Evangelidis, Jan Cech, Radu Horaud. (2014). Continuous Action Recognition Based on Sequence Alignment. International Journal of Computer Vision, 112(1), 90–114[10.1007/s11263-014-0758-9]
  23. 23.Mahmud Saif, Tanjid Hasan Tonmoy M., Kumar Bhaumik Kishor, Mahbubur Rahman A.K.M., Ashraful Amin M., Shoyaib Mohammad, Asif Hossain Khan Muhammad, Ahsan Ali Amin. (2020). Human Activity Recognition from Wearable Sensor Data Using Self-Attention. Frontiers in artificial intelligence and applications, 1332–1339[10.3233/faia200236]
  24. 24.T. Huang, Daniela Koller, Jitendra Malik, G. Ogasawara, B. V. Rao, Stuart Russell, Joseph Weber. (1994). Automatic symbolic traffic scene analysis using belief networks. , 966–972
  25. 25.Lombriser, Clemens, Bharatula, Nagendra Bhargava, Roggen, Daniel, Tröster, Gerhard. (2007). On-body activity recognition in a dynamic sensor network. , 17[10.3929/ethz-a-005704040]
  26. 26.Clemens Lombriser, Nagendra B. Bharatula, Daniel Roggen, Gerhard Tröster. (2007). On-body activity recognition in a dynamic sensor network. [10.4108/bodynets.2007.114]
  27. 27.Mehrsan Javan Roshtkhari, Martin D. Levine. (2013). Human activity recognition in videos using a single example. Image and Vision Computing, 31(11), 864–876[10.1016/j.imavis.2013.08.005]
  28. 28.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]
  29. 29.Amir-Hossein Shabani, David Clausi, John S. Zelek. (2011). Improved Spatio-temporal Salient Feature Detection for Action Recognition. , 100.1–100.0[10.5244/c.25.100]
  30. 30.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]
  31. 31.Md. Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, Mohammad Shoyaib. (2019). Can a simple approach identify complex nurse care activity?. , 736–740[10.1145/3341162.3344859]
  32. 32.Md. Nazmul Haque, Mahir Mahbub, Md. Hasan Tarek, Lutfun Nahar Lota, Amin Ahsan Ali. (2019). Nurse care activity recognition. , 719–723[10.1145/3341162.3344848]
  33. 33.Xin Cao, Wataru Kudo, Chihiro Ito, Masaki Shuzo, Eisaku Maeda. (2019). Activity recognition using ST-GCN with 3D motion data. , 689–692[10.1145/3341162.3345581]
  34. 34.Benyue Su, Huang Wu, Min Sheng. (2017). Human action recognition method based on hierarchical framework via Kinect skeleton data. , 19, 83–90[10.1109/icmlc.2017.8107747]
  35. 35.Mahmud, Saif, Tonmoy, M Tanjid Hasan, Bhaumik, Kishor Kumar, Rahman, A K M Mahbubur, Amin, M Ashraful, Shoyaib, Mohammad, Khan, Muhammad Asif Hossain, Ali, Amin Ahsan. (2020). Human Activity Recognition from Wearable Sensor Data Using Self-Attention. arXiv (Cornell University)[10.48550/arxiv.2003.09018]

Cited By

  1. 1.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]
  2. 2.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]
  3. 3.Koorosh Roohi, Atena Roshan Fekr. (2026). Deep learning-based recognition of simulated nursing activities for infection risk assessment. Machine Learning Health, 2(1), 015007[10.1088/3049-477x/ae382d]
← All publications