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Investigation of Sensor Placement for Accurate Fall Detection

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Wireless Mobile Communication and Healthcare (MobiHealth 2016)

Abstract

Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.

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References

  1. World Health Organization: Global report on falls prevention in older age. http://www.who.int/ageing/publications/Falls_prevention7March.pdf

  2. Gurley, R.J., Lum, N., Sande, M., Lo, B., Katz, M.H.: Persons found in their homes helpless or dead. N. Engl. J. Med. 334, 1710–1716 (1996)

    Article  Google Scholar 

  3. Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle, V., Lundy, J.E.: Fall detection—principles and methods. In: Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology, Lyon, France, pp. 1663–1666 (2007)

    Google Scholar 

  4. Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 10th International Conference on e-health Networking, Applications and Services, HealthCom, Singapore, pp. 42–47 (2008)

    Google Scholar 

  5. Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circuits Syst. Video Technol. 21, 611–622 (2011)

    Article  Google Scholar 

  6. Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. Real-Time Image Proc. 9, 635–646 (2012)

    Article  Google Scholar 

  7. Olivieri, D.N., Conde, I.G., Sobrino, X.A.V.: Eigenspace-based fall detection and activity recognition from motion templates and machine learning. Expert Syst. Appl. 39, 5935–5945 (2012)

    Article  Google Scholar 

  8. Yang, C., Hsu, Y.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10, 7772–7788 (2010)

    Article  Google Scholar 

  9. Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26, 194–199 (2007)

    Article  Google Scholar 

  10. Bourke, A.K., van de Ven, P., Gamble, M., O’Connor, R., Murphy, K., Bogan, E., McQuade, E., Finucane, P., Laighin, G., Nelson, J.: Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities. In: Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, pp. 2782–2785 (2010)

    Google Scholar 

  11. Özdemir, A.T., Barshan, B.: Detecting falls with wearable sensors using machine learning techniques. Sensors 14, 10691–10708 (2014)

    Article  Google Scholar 

  12. Kerdegari, H., Samsudin, K., Ramli, A.R., Mokaram, S.: Evaluation of fall detection classification approaches. In: 4th International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, pp. 131–136 (2012)

    Google Scholar 

  13. Yuwono, M., Moulton, B.D., Su, S.W., Celler, B.G., Nguyen, H.T.: Unsupervised machine-learning method for improving the performance of ambulatory fall detection systems. Biomed. Eng. Online 11, 1–11 (2012)

    Article  Google Scholar 

  14. Özdemir, A.T.: An analysis on sensor locations of the human body for wearable fall detection devices: principles and practice. Sensors 16, 1161 (2016)

    Article  Google Scholar 

  15. Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jamsa, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28, 285–291 (2008)

    Article  Google Scholar 

  16. Kangas M., Konttila A., Winblad I., Jamsa T.: Determination of simple thresholds for accelerometry-based parameters for fall detection. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 1367–1370 (2007)

    Google Scholar 

  17. Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med. Eng. Phys. 30, 84–90 (2008)

    Article  Google Scholar 

  18. 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. Rehabil. Eng. 18, 619–627 (2010)

    Article  Google Scholar 

  19. Abbate, S., Avvenuti, M., Corsini, P., Vecchio, A., Light, J.: Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey. In: Merret, G.V., Tan, Y.K. (eds.) Wireless Sensor Networks: Application-Centric Design, pp. 147–166. InTech, Rijeka (2010). Chapter 9

    Google Scholar 

  20. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explor. Newsl. 11, 10–18 (2009)

    Article  Google Scholar 

  21. Aha, D.W., Kibbler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  22. Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2, 18–22 (2002)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  24. Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Comput. 13, 637–649 (2001)

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported by the FrailSafe project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 690140. The paper reflects only the view of the authors and the Commission is not responsible for any use that may be made of the information it contains.

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Informatics, University of Patras, Rion, Patras, Greece

    Periklis Ntanasis, Evangelia Pippa & Vasileios Megalooikonomou

  2. Department of Electrical and Electronics Engineering, Erciyes University, Melikgazi, 38039, Kayseri, Turkey

    Ahmet Turan Özdemir

  3. Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, 06800, Ankara, Turkey

    Billur Barshan

Authors
  1. Periklis Ntanasis
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  2. Evangelia Pippa
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  3. Ahmet Turan Özdemir
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  4. Billur Barshan
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  5. Vasileios Megalooikonomou
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Corresponding author

Correspondence to Periklis Ntanasis .

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Editors and Affiliations

  1. Polytechnic University of Milan, Milan, Italy

    Paolo Perego

  2. Polytechnic University of Milan, Milan, Italy

    Giuseppe Andreoni

  3. Institute of Molecular Bioimaging and Physiology, Segrate, Italy

    Giovanna Rizzo

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ntanasis, P., Pippa, E., Özdemir, A.T., Barshan, B., Megalooikonomou, V. (2017). Investigation of Sensor Placement for Accurate Fall Detection. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-58877-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58876-6

  • Online ISBN: 978-3-319-58877-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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