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CNN for Elderly Wandering Prediction in Indoor Scenarios

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Abstract

This work proposes a way to detect the wandering movement of Alzheimer’s patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.

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References

  1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G.S, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X. TensorFlow: large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. 2015.

  2. Akbar MF, Putrada AG, Abdurohman M. Smart light recommending system using artificial neural network algorithm. In: 2019 7th International conference on information and communication technology (ICoICT). IEEE; 2019. p. 1–5. https://doi.org/10.1109/ICoICT.2019.8835192

  3. Amin J, Sharif M, Anjum MA, Raza M, Bukhari SAC. Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cogn Syst Res. 2020;59:304–11.

    Article  Google Scholar 

  4. Amin J, Sharif M, Gul N, Raza M, Anjum MA, Nisar MW, Bukhari SAC. Brain tumor detection by using stacked autoencoders in deep learning. J Med Syst. 2020;44(2):32.

    Article  Google Scholar 

  5. Arifoglu D, Bouchachia A. Activity recognition and abnormal behaviour detection with recurrent neural networks. Procedia Comput Sci. 2017;110:86–93.

    Article  Google Scholar 

  6. Ballard CG, Gauthier S, Cummings JL, Brodaty H, Grossberg GT, Robert P, Lyketsos CG. Management of agitation and aggression associated with Alzheimer disease. Nat Rev Neurol. 2009;5(5):245.

    Article  Google Scholar 

  7. Boutoleau-Bretonnière C, Pouclet-Courtemanche H, Gillet A, Bernard A, Deruet A-L, Gouraud I, Lamy E, Mazoué A, Rocher L, Bretonnière C, et al. Impact of confinement on the burden of caregivers of patients with the behavioral variant of frontotemporal dementia and Alzheimer disease during the COVID-19 crisis in France. Dement Geriatr Cogn Disord Extra. 2020;10(3):127–34.

    Article  Google Scholar 

  8. Chollet F. Deep learning with Python. 1st ed. Shelter Island: Manning Publications Co.; 2017.

    Google Scholar 

  9. Chollet F. et al. Keras: the Python deep learning library. 2018. Library available at https://keras.io/. Accessed 28 Mar 2022.

  10. Courtney KL. Privacy and senior willingness to adopt smart home information technology in residential care facilities. Methods Inf Med. 2008;47(01):76–81.

    Article  Google Scholar 

  11. Delaunay A, Guérin J. Wandering detection within an embedded system for Alzheimer suffering patients. In: 2017 AAAI spring symposium series; 2017. https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15317 of subordinate document. Accessed 28 Mar 2022.

  12. Doughty K, Williams G, King P, Woods R. Diana-a telecare system for supporting dementia sufferers in the community. In: Proceedings of the 20th annual international conference of the IEEE engineering in medicine and biology society, vol. 20. Biomedical engineering towards the year 2000 and beyond (Cat. No. 98CH36286), vol. 4. IEEE; 1998. p. 1980–1983.

  13. Gadde R, Jampani V, Kiefel M, Kappler D, Gehler PV. Superpixel convolutional networks using bilateral inceptions. In: European conference on computer vision. Berlin: Springer; 2016. p. 597–613.

  14. Gemmeke JF, Ellis DPW, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M. Audio set: an ontology and human-labeled dataset for audio events. In: Proc. IEEE ICASSP 2017, New Orleans, LA; 2017. https://doi.org/10.1109/ICASSP.2017.7952261.

  15. Ghosh S, Chaudhury KN. Color bilateral filtering using stratified Fourier sampling. In: 2018 IEEE global conference on signal and information processing (GlobalSIP). IEEE; 2018. p. 26–30. https://doi.org/10.1109/GlobalSIP.2018.8646671.

  16. Ghosh S, Nair P, Chaudhury KN. Optimized Fourier bilateral filtering. IEEE Signal Process Lett. 2018;25(10):1555–9.

    Article  Google Scholar 

  17. Gron A. Hands-on machine learning with scikit-learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. 1st ed. Newton: O’Reilly Media, Inc.; 2017.

    Google Scholar 

  18. Han J, Haihong E, Le G, Du J. Survey on NoSQL database. In: 2011 6th International conference on pervasive computing and applications. IEEE; 2011. p. 363–6. https://doi.org/10.1109/ICPCA.2011.6106531.

  19. Karimi K, Atkinson G. What the internet of things (IoT) needs to become a reality. White Paper, FreeScale and ARM; 2013. p. 1–16.

  20. Khan SS, Ye B, Taati B, Mihailidis A. Detecting agitation and aggression in people with dementia using sensors—a systematic review. Alzheimers Dement. 2018;14(6):824–32.

    Article  Google Scholar 

  21. Li J, Dai W, Metze F, Qu S, Das S. A comparison of deep learning methods for environmental sound detection. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP); 2017. p. 126–30. https://doi.org/10.1109/ICASSP.2017.7952131.

  22. Light RA. Mosquitto: server and client implementation of the MQTT protocol. J Open Source Softw. 2017;2(13):265.

    Article  Google Scholar 

  23. Lin Q, Zhang D, Chen L, Ni H, Zhou X. Managing elders’ wandering behavior using sensors-based solutions: a survey. Int J Gerontol. 2014;8(2):49–55.

    Article  Google Scholar 

  24. Lin Q, Zhang D, Huang X, Ni H, Zhou X. Detecting wandering behavior based on GPS traces for elders with dementia. In: 2012 12th International conference on control automation robotics and vision (ICARCV). IEEE; 2012. p. 672–7. https://doi.org/10.1109/ICARCV.2012.6485238.

  25. Martino-Saltzman D, Blasch BB, Morris RD, McNeal LW. Travel behavior of nursing home residents perceived as wanderers and nonwanderers. Gerontologist. 1991;31(5):666–72.

    Article  Google Scholar 

  26. Masse M. REST API design rulebook: designing consistent RESTful web service interfaces. Newton: O’Reilly Media, Inc.; 2011.

    Google Scholar 

  27. Oliveira R, Barreto F, Abreu R. Convolutional neural network for elderly wandering prediction in indoor scenarios. In: Proceedings of the 14th international joint conference on biomedical engineering systems and technologies—HEALTHINF. INSTICC, SciTePress; 2021. p. 253–60. https://doi.org/10.5220/0010379902530260.

  28. O’Neil ME, Freeman M, Christensen V, Telerant R, Addleman A, Kansagara D, et al. A systematic evidence review of non-pharmacological interventions for behavioral symptoms of dementia. Washington, DC: Department of Veterans Affairs; 2011.

    Google Scholar 

  29. Oniga S, Sütő J. Human activity recognition using neural networks. In: Proceedings of the 2014 15th international Carpathian control conference (ICCC). IEEE; 2014. p. 403–6. https://doi.org/10.1109/CarpathianCC.2014.6843636.

  30. Ordóñez FJ, Roggen D. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors. 2016;16(1):115.

    Article  Google Scholar 

  31. Pham VT, Qiu Q, Wai AAP, Biswas J. Application of ultrasonic sensors in a smart environment. Pervasive Mob Comput. 2007;3(2):180–207.

    Article  Google Scholar 

  32. Piczak KJ. Environmental sound classification with convolutional neural networks. In: 2015 IEEE 25th international workshop on machine learning for signal processing (MLSP); 2015. p. 1–6. https://doi.org/10.1109/MLSP.2015.7324337.

  33. Radziszewski R, Ngankam HK, Grégoire V, Lorrain D, Pigot H, Giroux S. Designing calm and non-intrusive ambient assisted living system for monitoring nighttime wanderings. Int J Pervasive Comput Commun. 2017;13(2):114–29.

    Article  Google Scholar 

  34. Severance C. Discovering javascript object notation. Computer. 2012;45(4):6–8.

    Article  Google Scholar 

  35. Shirley P, Marschner S. Fundamentals of computer graphics. 3rd ed. Natick: A. K. Peters, Ltd.; 2009.

    Book  Google Scholar 

  36. Singh NA, Borschbach M. Effect of external factors on accuracy of distance measurement using ultrasonic sensors. In: 2017 International conference on signals and systems (ICSigSys); 2017. p. 266–71. https://doi.org/10.1109/ICSIGSYS.2017.7967054.

  37. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–58.

    MathSciNet  MATH  Google Scholar 

  38. Talathi SS, Vartak A. Improving performance of recurrent neural network with ReLU nonlinearity. 2015. arXiv preprint; arxiv:1511.03771 of subordinate document. Accessed 28 Mar 2022.

  39. Teri L, Larson EB, Reifler BV. Behavioral disturbance in dementia of the Alzheimer’s type. J Am Geriatr Soc. 1988;36(1):1–6.

    Article  Google Scholar 

  40. Vogado LH, Veras RM, Aires KR. “LeukNet”—a model of convolutional neural network for the diagnosis of leukemia. In: Anais Estendidos do XXXIII conference on graphics, patterns and images. SBC; 2020. p. 119–25. https://doi.org/10.5753/sibgrapi.est.2020.12993.

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Acknowledgements

The authors would like to thank Unilasalle-RJ for encouraging and financially supporting this work.

Funding

This work was partially funded by UNILASALLE-RJ.

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Authors and Affiliations

  1. Centro Universitário Unilasalle do Rio de Janeiro, R. Gastão Gonçalves, 79, Niterói, Rio de Janeiro, Brazil

    Rafael Oliveira, Rafael Feres, Fabio Barreto & Raphael Abreu

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  1. Rafael Oliveira
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Correspondence to Rafael Oliveira.

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This article is part of the topical collection “Biomedical Engineering Systems and Technologies” guest edited by Hugo Gamboa and Ana Fred.

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Oliveira, R., Feres, R., Barreto, F. et al. CNN for Elderly Wandering Prediction in Indoor Scenarios. SN COMPUT. SCI. 3, 230 (2022). https://doi.org/10.1007/s42979-022-01091-3

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