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Classifying Human Activities with Temporal Extension of Random Forest

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

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Abstract

Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~98 %.

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References

  1. Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inf. 17(3), 579–590 (2013)

    Article  Google Scholar 

  2. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 790–808 (2012)

    Article  Google Scholar 

  3. Guan, Q., Li, C., Guo, X., Wang, G.: Compressive classification of human motion using pyroelectric infrared sensors. Pattern Recogn. Lett. 49, 231–237 (2014)

    Article  Google Scholar 

  4. Jatobá, L.C., Großmann, U., Kunze, C., Ottenbacher, J., Stork, W.: Pattern recognition methods for classification of physical activity. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5250–5253 (2008)

    Google Scholar 

  5. Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)

    Article  Google Scholar 

  6. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. San-Segundo, R., Lorenzo-Trueba, J., Martínez-González, B., Pardo, J.M.: Segmenting human activities based on HMMs using smartphone inertial sensors. Pervasive Mob. Comput. 30, 84–96 (2016)

    Article  Google Scholar 

  8. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphone-based activity recognition using fixed-point arithmetic. Special session in ambient assisted living: home care. J. Univ. Comput. Sci. 19(9), 1295–1314 (2013)

    Google Scholar 

  9. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 24–26 (2013)

    Google Scholar 

  10. Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2015)

    Article  Google Scholar 

  11. Ronao, C.A., Cho, S.-B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59, 235–244 (2016)

    Article  Google Scholar 

  12. Wu, D., Wang, Z., Chen, Y., Zhao, H.: Neurocomputing mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 190, 35–49 (2016)

    Article  Google Scholar 

  13. Chamroukhi, F., Mohammed, S., Trabelsi, D., Oukhellou, L., Amirat, Y.: Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing 120, 633–644 (2013)

    Article  Google Scholar 

  14. Sanchez-Valdes, D., Alvarez-Alvarez, A., Trivino, G.: Dynamic linguistic descriptions of time series applied to self-track the physical activity. Fuzzy Sets Syst. 1, 1–20 (2015)

    Google Scholar 

  15. Liu, L., Peng, Y., Liu, M., Huang, Z.: Sensor-based human activity recognition system with a multilayered model using time series shapelets. Knowl. Based Syst. 90, 138–152 (2015)

    Article  Google Scholar 

  16. Liu, L., Peng, Y., Wang, S., Liu, M., Huang, Z.: Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors. Inf. Sci. 340–341, 1–17 (2016)

    Article  MathSciNet  Google Scholar 

  17. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  18. Ooi, S.Y., Tan, S.C., Cheah, W.P.: Temporal Sampling Forest (TSF): an ensemble temporal learner. Soft. Comput. (2016). doi:10.1007/s00500-016-2242-7

    Google Scholar 

  19. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., Burlington (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia

    Shih Yin Ooi, Shing Chiang Tan & Wooi Ping Cheah

Authors
  1. Shih Yin Ooi
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  2. Shing Chiang Tan
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  3. Wooi Ping Cheah
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Corresponding author

Correspondence to Shih Yin Ooi .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Ooi, S.Y., Tan, S.C., Cheah, W.P. (2016). Classifying Human Activities with Temporal Extension of Random Forest. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

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