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Deep Human Action Recognition System for Assistance of Alzheimer’s Patients

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Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

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Abstract

People with Alzheimer’s disease find it difficult to perform their Activities of Daily Living (ADLs) due to limited abilities in cognitive functioning. They cannot remember the correct sequence of the performed activity steps, and sometimes they can spend a lot of time in an activity without finishing it. Therefore, to be successful with ADLs, they need human caregivers, but this reduces their autonomy. In response to these needs of the elderly, we have developed an assistance system for people with Alzheimer. The assistance system aims to ensure ADLs (like drinking beverage) performing correctly, based on action recognition from a video. Our assistance system provides patient assistance prompts as needed. The DemCare dataset was used to develop and validate the proposed assistance system. Obtained results are very promising and are comparable to the state-of-the-art approaches ones.

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References

  1. Jean-Baptiste, E.M.D., Mihailidis, A.: Benefits of automatic human recognition in an assistive system for people with dementia. In: IEEE Canada International Humanitarian Technology Conference (IHTC), Toronto, ON, Canada, pp. 61–65 (2017)

    Google Scholar 

  2. Peters, C., Hermann, T., Wachsmuth, S., Hoey, J.: Automatic task assistance for people with cognitive disabilities in brushing teeth – a user study with the TEBRA system. ACM Trans. Accessible Comput. 5(4), 1–34 (2014)

    Article  Google Scholar 

  3. Jean-Baptiste, E., Howe, J., Rotshtein, P., Russell, M.: CogWatch: intelligent agent-based system to assist stroke survivors during tea-making. In: SAI Intelligent Systems Conference (IntelliSys), London, UK, pp. 452–456 (2015)

    Google Scholar 

  4. Jean-Baptiste, E., Russell, M., Howe, J., Rotshtein, P.: Intelligent prompting system to assist stroke survivors. J. Ambient Intell. Smart Environ. 9(6), 707–723 (2017)

    Article  Google Scholar 

  5. Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthcare Eng. 1–31 (2017)

    Google Scholar 

  6. Gnouma, M., Ladjailia, A., Ejbali, R., Zaied, M.: Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimedia Tools Appl. 78(5), 1–23 (2019)

    Google Scholar 

  7. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), Beijing, China, pp. 1395–1402 (2005)

    Google Scholar 

  8. Ladjailia, A., Bouchrika, I., Merouani, H., Harrati, N., Mahfouf, Z.: Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput. Appl. 115, 1–14 (2019)

    Google Scholar 

  9. Avgerinakis, K., Briassouli, A., Kompatsiaris, Y.: Activity detection using sequential statistical boundary detection (SSBD). Comput. Vis. Image Underst. 144, 46–61 (2016)

    Article  Google Scholar 

  10. Poularakis, S., Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Efficient motion estimation methods for fast recognition of activities of daily living. Signal Process. Image Commun. 53, 1–12 (2017)

    Article  Google Scholar 

  11. Laptev, I., Caputo, B., Schüldt, C., Lindeberg, T.: Local velocity adapted motion events for spatio-temporal recognition. Comput. Vis. Image Underst. 108(3), 207–229 (2007)

    Article  Google Scholar 

  12. Jlidi, N., Snoun, A., Bouchrika, T., Jemai, O., Zaied, M.: PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations. In: Twelfth International Conference on Machine Vision (ICMV 2019), Amsterdam, The Netherlands, p. 114330 (2020)

    Google Scholar 

  13. Xia, L., Chen, C.-C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, pp. 20–27 (2012)

    Google Scholar 

  14. Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans. Image Process. 29, 9532–9545 (2020)

    Article  Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770–778 (2016)

    Google Scholar 

  16. Teyeb, I., Snoun, A., Jemai, O., Zaied, M.: Fuzzy logic decision support system for hypovigilance detection based on CNN feature extractor and WN classifier. J. Comput. Sci. (JCS) 14(11), 1546–1564 (2018)

    Article  Google Scholar 

  17. Ejbali, R., Jemai, O., Zaied, M.: A multiresolution wavelet networks architecture and its application to pattern recognition. Pattern Recogn. Image Anal. (PRIA) 27(3), 494–510 (2017)

    Article  Google Scholar 

  18. Snoun, A., Teyeb, I., Jemai, O., Zaied, M.: A multimodal vigilance monitoring system based on fuzzy logic architecture. In: 24th International Conference on Neural Information Processing (ICONIP), Guangzhou, Chine, pp. 202–211 (2017)

    Google Scholar 

  19. Yahia, S., Said, S., Jemai, O., Zaied, M., Ben Amar, C.: Comparison between extreme learning machine and wavelet neural networks in data classification. In: 9th International Conference on Machine Vision (ICMV), Nice, France, vol. 10341, pp. 103412K-1–103412K-5 (2017)

    Google Scholar 

  20. Said, S., Jemai, O., Hsairi, S., Ejbeli, R., Zaied, M., Ben Amar, C.: Deep wavelet network for image classification. In: IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC), Budapest, Hongrie, pp. 922–927 (2016)

    Google Scholar 

  21. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  22. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  23. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  24. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  25. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)

    Article  Google Scholar 

  26. Dem@care dataset, Thessaloniki, Greece. https://www.demcare.eu/results/datasets

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Acknowledgement

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

Author information

Authors and Affiliations

  1. Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes (ENIG), University of Gabes, Gabes, Tunisia

    Rimeh Jarray, Ahmed Snoun, Tahani Bouchrika & Olfa Jemai

  2. Higher Institute of Computer Science and Multimedia Gabes (ISIMG), University of Gabes, Gabes, Tunisia

    Rimeh Jarray & Olfa Jemai

  3. Faculty of Science of Gabes (FSG), University of Gabes, Gabes, Tunisia

    Tahani Bouchrika

Authors
  1. Rimeh Jarray
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  2. Ahmed Snoun
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  3. Tahani Bouchrika
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  4. Olfa Jemai
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Corresponding author

Correspondence to Rimeh Jarray .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Jarray, R., Snoun, A., Bouchrika, T., Jemai, O. (2021). Deep Human Action Recognition System for Assistance of Alzheimer’s Patients. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_49

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