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A Model for Human Activity Recognition in Ambient Assisted Living

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 96))

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Abstract

This work presents a model for human activity recognition, through an IoT paradigm, using location and movement data, generated from an accelerometer. The activities of five individuals from different age groups were monitored, utilizing IoT devices, using the activities of four of these individuals to train the model and the activities of the remaining individual for test data. For the prediction of the activities, the Extra Trees algorithm was used, where the results of 81.16% accuracy were obtained when only movement data were used, 92.59% when using both movement and location data, and 97.56% when using movement data and synthetic location data.

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References

  1. IBGE: Projeção da população do brasil por sexo e idade 2000-2060. Revisão 2013 (2013)

    Google Scholar 

  2. Forkan, A.R.M., Khalil, I., Tari, Z., Foufou, S., Bouras, A.: A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognit. 48(3), 628–641 (2015)

    Article  Google Scholar 

  3. Amaral, W.D., Dantas, M.: Um modelo de reconhecimento de atividades humanas baseado no uso de acelerômetro com qoc. In: Workshop de Iniciação Científica do WSCAD 2017 (XVIII Simpósio em Sistemas Computacionais de Alto Desempenho), pp. 45–50 (2017)

    Google Scholar 

  4. Mario, M.: Human activity recognition based on single sensor square HV acceleration images and convolutional neural networks. IEEE Sens. J. 19, 1487–1498 (2018)

    Article  Google Scholar 

  5. Choi, H., Wang, Q., Toledo, M., Turaga, P., Buman, M., Srivastava, A.: Temporal alignment improves feature quality: an experiment on activity recognition with accelerometer data. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, pp. 462–4628 (2018)

    Google Scholar 

  6. Dwiyantoro, A.P.J., Nugraha, I.G.D., Choi, D.: A simple hierarchical activity recognition system using a gravity sensor and accelerometer on a smartphone. Int. J. Technol. 7(5), 831–839 (2016)

    Article  Google Scholar 

  7. Murao, K., Terada, T.: A combined-activity recognition method with accelerometers. J. Inf. Process. 24(3), 512–521 (2016)

    Google Scholar 

  8. Kim, Y.J., Kang, B.N., Kim, D.: Hidden Markov model ensemble for activity recognition using tri-axis accelerometer. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3036–3041 (2015)

    Google Scholar 

  9. Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2006, pp 113–116. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  10. Atallah, L., Lo, B., King, R., Yang, G.Z.: Sensor positioning for activity recognition using wearable accelerometers. IEEE Trans. Biomed. Circuits Syst. 5(4), 320–329 (2011)

    Article  Google Scholar 

  11. Noor, M.H.M., Salcic, Z., Wang, K.I.-K.: Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Pervasive Mob. Comput. 38(1), 41–59 (2017)

    Article  Google Scholar 

  12. Chevalier, G.: LSTMS for human activity recognition (2016). https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition. Accessed 13 June 2017

  13. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21th European Symposium on Artificial Neural Networks. ESANN, Computational Intelligence and Machine Learning (2013)

    Google Scholar 

  14. Zhu, C., Sheng, W.: Motion- and location-based online human daily activity recognition (2013)

    Google Scholar 

  15. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  16. Nazário, D.C., Campos, P.J., Inacio, E.C., Dantas, M.A.R.: Quality of context evaluating approach in AAL environment using IoT technology. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, pp. 558–563 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Science, Engineering and Information Technology, Federation University Australia, Brisbane, Australia

    Wagner D. do Amaral

  2. Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Mario A. R. Dantas & Fernanda Campos

Authors
  1. Wagner D. do Amaral
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  2. Mario A. R. Dantas
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  3. Fernanda Campos
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Corresponding author

Correspondence to Wagner D. do Amaral .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Antwerp, Antwerpen, Belgium

    Peter Hellinckx

  3. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

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do Amaral, W.D., Dantas, M.A.R., Campos, F. (2020). A Model for Human Activity Recognition in Ambient Assisted Living. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-33509-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33508-3

  • Online ISBN: 978-3-030-33509-0

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