Abstract
Falls are a major health problem among the elderly. The consequences of a fall can be minimized by an early detection. In this sense, there is an emerging trend towards the development of agent systems based on mobile phones for fall detection. But when a mobile phone-based fall detector is used in a real-world scenario, the specific features of the phone can affect the performance of the system. This study aims to clarify the impact of two features: the accelerometer sampling frequency and the way the mobile phone is carried. In this experimental study, 5 participants have simulated different falls and activities of daily living. Using these data, the study shows that the sampling frequency affects the performance of the detection. In the same way, when a fall detector intended to be attached at the body is carried in an external accessory, the performance of the system decreases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rubenstein, L.Z., Josephson, K.R.: Falls and their prevention in elderly people: What does the evidence show? Med. Clin. North. Am. 90(5), 807–824 (2006)
Masud, T., Morris, R.O.: Epidemiology of falls. Age Ageing 30(4), 3–7 (2001)
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.: A survey of mobile phone sensing. IEEE Commun. Magazine 48(9), 140–150 (2010)
O’Grady, M.J., O’Hare, G.M.P.: Mobile devices and intelligent agents - towards a new generation of applications and services. Information Sciences 171, 335–353 (2005)
Sposaro, F., Tyson, G.: iFall: An Android application for fall monitoring and response. In: Proc. IEEE Engineering in Medicine and Biology Society, EMBC, pp. 6119–6122 (2009)
Dai, J., Bai, X., Yang, Z., Shen, Z., Xuan, D.: Mobile phone-based pervasive fall detection. Personal Ubiquitous Comput. 14(7), 633–643 (2010)
Lee, R.Y.W., Carlisle, A.J.: Detection of falls using accelerometers and mobile phone technology. Age and Ageing, 1–7 (2011)
Albert, M.V., Kording, K., Herrmann, M., Jayaraman, A.: Fall classification by machine learning using mobile phones. PLoS ONE 7(5), e36556 (2012)
Martín, P., Sánchez, M., Álvarez, L., Alonso, V., Bajo, J.: Multi-Agent System for Detecting Elderly People Falls through Mobile Devices. In: Novais, P., Preuveneers, D., Corchado, J.M. (eds.) ISAmI 2011. AISC, vol. 92, pp. 93–99. Springer, Heidelberg (2011)
Sánchez, M., Martín, P., Álvarez, L., Alonso, V., Zato, C., Pedrero, A., Bajo, J.: A New Adaptive Algorithm for Detecting Falls through Mobile Devices. In: Corchado, J.M., Pérez, J.B., Hallenborg, K., Golinska, P., Corchuelo, R. (eds.) Trends in PAAMS. AISC, vol. 90, pp. 17–24. Springer, Heidelberg (2011)
Fang, S.H., Liang, Y.C., Chiu, K.M.: Developing a Mobile Phone-based Fall Detection Sys-tem on Android Platform. In: Proc. Computing, Communications and Applications, ComComAp, pp. 143–146 (2012)
Lopes, I.C., Vaidya, B., Rodrigues, J.: Towards an autonomous fall detection and alerting system on a mobile and pervasive environment. Telecommun. Syst. 1–12 (2011)
Noury, N., Rumeau, P., Bourke, A.K., OLaighin, G., Lundy, J.E.: A proposal for the classication and evaluation of fall detectors. IRBM 29(6), 340–349 (2008)
O’Neill, T.W., et al.: Age and sex influences on fall characteristics. Ann. Rheum. Dis. 53, 773–775 (1994)
Fawcett, T.: ROC graphs: Notes and practical considerations for data mining researchers. Technical Report (2003), http://binf.gmu.edu/mmasso/ROC101.pdf (accessed December 2012)
Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture 28(2), 285–291 (2008)
Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based triaxial accelerometer fall detection algorithm. Gait & Posture 26(2), 194–199 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Igual, R., Medrano, C., Martin, L., Plaza, I. (2013). Mobile Phone-Based Fall Detectors: Ready for Real-World Scenarios?. In: Corchado, J.M., et al. Highlights on Practical Applications of Agents and Multi-Agent Systems. PAAMS 2013. Communications in Computer and Information Science, vol 365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38061-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-38061-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38060-0
Online ISBN: 978-3-642-38061-7
eBook Packages: Computer ScienceComputer Science (R0)