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Fall Detection Analysis Using a Real Fall Dataset

  • Samad Barri Khojasteh
  • José R. VillarEmail author
  • Enrique de la Cal
  • Víctor M. González
  • Javier Sedano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 771)

Abstract

This study focuses on the performance of a fall detection method using data coming from real falls performed by relatively young people and the application of this technique in the case of an elder person. Although the vast majority of studies concerning fall detection place the sensory on the waist, in this research the wearable device must be placed on the wrist because it’s usability. A first pre-processing stage is carried out as stated in [1, 17]; this stage detects the most relevant points to label. This study analyzes the suitability of different models in solving this classification problem: a feed-forward Neural Network and a rule based system generated with the C5.0 algorithm. A discussion about the results and the deployment issues is included.

Notes

Acknowledgment

This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2014-56967-R and MINECO-TIN2017-84804-R.

References

  1. 1.
    Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., Vecchio, A.: A smartphone-based fall detection system. Pervasive Mob. Comput. 8(6), 883–899 (2012)CrossRefGoogle Scholar
  2. 2.
    Abbate, S., Avvenuti, M., Corsini, P., Light, J., Vecchio, A.: Wireless Sensor Networks: Application - Centric Design. In: Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey, p. 22. Intech (2010)Google Scholar
  3. 3.
    Bianchi, F., Redmond, S.J., Narayanan, M.R., Cerutti, S., Lovell, N.H.: Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans. Neural Syst. Rehab. Eng. 18(6), 619–627 (2010)CrossRefGoogle Scholar
  4. 4.
    Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based triaxial accelerometer fall detection algorithm. Gait Posture 26, 194–199 (2007)CrossRefGoogle Scholar
  5. 5.
    Casilari, E., Santoyo-Ramón, J.A., Cano-García, J.M.: Umafall: a multisensor dataset for the research on automatic fall detection. Procedia Comput. Sci. 110(Supplement C), 32–39 (2017). http://www.sciencedirect.com/science/article/pii/S1877050917312899CrossRefGoogle Scholar
  6. 6.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)zbMATHGoogle Scholar
  7. 7.
    Daher, M., Diab, A., Najjar, M.E.B.E., Khalil, M.A., Charpillet, F.: Elder tracking and fall detection system using smart tiles. IEEE Sens. J. 17(2), 469–479 (2017). http://ieeexplore.ieee.org/document/7733127/CrossRefGoogle Scholar
  8. 8.
    Delahoz, Y.S., Labrador, M.A.: Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10), 19806–19842 (2014). http://www.mdpi.com/1424-8220/14/10/19806/htmCrossRefGoogle Scholar
  9. 9.
    Fang, Y.C., Dzeng, R.J.: A smartphone-based detection of fall portents for construction workers. Procedia Eng. 85, 147–156 (2014)CrossRefGoogle Scholar
  10. 10.
    Fang, Y.C., Dzeng, R.J.: Accelerometer-based fall-portent detection algorithm for construction tiling operation. Autom. Constr. 84, 214–230 (2017)CrossRefGoogle Scholar
  11. 11.
    González, S., Sedano, J., Villar, J.R., Corchado, E., Herrero, Á., Baruque, B.: Features and models for human activity recognition. Neurocomputing 167, 52–60 (2015)CrossRefGoogle Scholar
  12. 12.
    Hakim, A., Huq, M.S., Shanta, S., Ibrahim, B.: Smartphone based data mining for fall detection: Analysis and design. Procedia Comput. Sci. 105, 46–51 (2017). http://www.sciencedirect.com/science/article/pii/S1877050917302065CrossRefGoogle Scholar
  13. 13.
    Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q.: Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sens. 2015, 8 (2015)CrossRefGoogle Scholar
  14. 14.
    Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. BioMed. Eng. OnLine 12(1), 66 (2013). http://www.biomedical-engineering-online.com/content/12/1/66CrossRefGoogle Scholar
  15. 15.
    Igual, R., Medrano, C., Plaza, I.: A comparison of public datasets for acceleration-based fall detection. Med. Eng. Phys. 37(9), 870–878 (2015). http://www.sciencedirect.com/science/article/pii/S1350453315001575CrossRefGoogle Scholar
  16. 16.
    Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsaä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28, 285–291 (2008)CrossRefGoogle Scholar
  17. 17.
    Khojasteh, S.B., Villar, J.R., de la Cal, E., González, V.M., Sedano, J., Yazg̈an, H.R.: Evaluation of a Wrist-based Wearable Fall Detection Method. In: 13th International Conference on Soft Computing Models in Industrial and Environmental Applications (2018, submitted)Google Scholar
  18. 18.
    Kuhn, M.: The caret package (2017). http://topepo.github.io/caret/index.html. Accessed 15 Jan 2018
  19. 19.
    Kumari, P., Mathew, L., Syal, P.: Increasing trend of wearables and multimodal interface for human activity monitoring: a review. Biosens. Bioelectron. 90(15), 298–307 (2017)CrossRefGoogle Scholar
  20. 20.
    Purch.com: Top ten reviews for fall detection of seniors (2018). http://www.toptenreviews.com/health/senior-care/best-fall-detection-sensors/
  21. 21.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). http://www.R-project.org. ISBN 3-900051-07-0
  22. 22.
    Sabatini, A.M., Ligorio, G., Mannini, A., Genovese, V., Pinna, L.: Prior-to- and post-impact fall detection using inertial and barometric altimeter measurements. IEEE Trans. Neural Syst. Rehab. Eng. 24, 774–783 (2016)CrossRefGoogle Scholar
  23. 23.
    Sorvala, A., Alasaarela, E., Sorvoja, H., Myllyla, R.: A two-threshold fall detection algorithm for reducing false alarms. In: Proceedings of 2012 6th International Symposium on Medical Information and Communication Technology (ISMICT) (2012)Google Scholar
  24. 24.
    Vergara, P.M., de la Cal, E., Villar, J.R., González, V.M., Sedano, J.: An iot platform for epilepsy monitoring and supervising. J. Sens. 2017, 18 (2017)CrossRefGoogle Scholar
  25. 25.
    Villar, J.R., González, S., Sedano, J., Chira, C., Trejo, J.: Human Activity Recognition and Feature Selection for Stroke Early Diagnosis. In: Pan, J.S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS (LNAI), vol. 8073, pp. 659–668. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40846-5_66Google Scholar
  26. 26.
    Villar, J.R., González, S., Sedano, J., Chira, C., Trejo-Gabriel-Galán, J.M.: Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst. 25(4), 1450036–1450055 (2015)CrossRefGoogle Scholar
  27. 27.
    Wu, F., Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemedicine Appl. 2015, 11 (2015). https://www.hindawi.com/journals/ijta/2015/576364/Google Scholar
  28. 28.
    Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthc. Eng. 2017, 31 (2017)Google Scholar
  29. 29.
    Zhang, T., Wang, J., Xu, L., Liu, P.: Fall detection by wearable sensor and one-class svm algorithm. In: Huang, D.S., Li, K., Irwin, G. (eds.) Intelligent Computing in Signal Processing and Pattern Recognition, Lecture Notes in Control and Information Systems, vol. 345, pp. 858–863. Springer, Berlin Heidelberg (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Samad Barri Khojasteh
    • 1
    • 2
  • José R. Villar
    • 2
    Email author
  • Enrique de la Cal
    • 2
  • Víctor M. González
    • 3
  • Javier Sedano
    • 4
  1. 1.Department of Industrial EngineeringSakarya UniversitySerdivanTurkey
  2. 2.Computer Science Department, EIMEMUniversity of OviedoOviedoSpain
  3. 3.Control and Automatica Department, EPIUniversity of OviedoGijónSpain
  4. 4.Instituto Tecnológico de Castilla y LeónBurgosSpain

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