Abstract
Falls are a major threat for senior citizens living independently. Sensor technologies and fall detection algorithms have emerged as a reliable, low-cost solution for this issue. We proposed a sensor orientation calibration algorithm to better address the uncertainty issue faced by fall detection algorithms in real world applications. We conducted controlled experiments of simulated fall events and non-fall activities on student subjects. We evaluated our proposed algorithm using sequence matching based machine learning approaches on five different body positions. The algorithm achieved an F-measure of 90 to 95% in detecting falls. Sensors worn as necklace pendants or in chest pockets performed best.
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
Similar content being viewed by others
References
World Health Organization: Ageing and Life Course Unit, “WHO Global Report on Falls Prevention in Older Age”, World Health Organization (2008)
Tinetti, M.: Predictors and prognosis of inability to get up after falls among elderly persons. JAMA 269(1), 65 (1993)
Scheffer, A., Schuurmans, M., van Dijk, N., van der Hooft, T., de Rooij, S.: Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 37(1), 19–24 (2007)
Stevens, J.: Falls among older adults—risk factors and prevention strategies. J. Saf. Res. 36(4), 409–411 (2005)
Kale, N., Lee, J., Lotfian, R., Jafari, R.: Impact of sensor misplacement on dynamic time warping based human activity recognition using wearable computers. In: Proceedings of the Conference on Wireless Health. ACM (2012)
Henpraserttae, A., Thiemjarus, S., Marukatat, S.: Accurate activity recognition using a mobile phone regardless of device orientation and location. In: 2011 International Conference on Body Sensor Networks (2011)
Mizell, D.: Using gravity to estimate accelerometer orientation. In: Proceedings of the 7th IEEE International Symposium on Wearable Computers, ISWC 2003. IEEE Computer Society (2003)
Yang, J.: Toward physical activity diary. In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics - IMCE 2009 (2009)
Morales, J., Akopian, D., Agaian, S.: Human activity recognition by smartphones regardless of device orientation. In: Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2014 (2014)
“WHO|Falls”, Who.int. http://www.who.int/violence_injury_prevention/other_injury/falls/en/. Accessed 11 May 2016
Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)
Lee, J., Robinovitch, S., Park, E.: Inertial sensing-based pre-impact detection of falls involving near-fall scenarios. IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 258–266 (2015)
Bagalà, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J., Zijlstra, W., Klenk, J.: Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7(5), e37062 (2012)
Özdemir, A., Barshan, B.: Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6), 10691–10708 (2014)
Lan, M., Nahapetian, A., Vahdatpour, A., Au, L., Kaiser, W., Sarrafzadeh, M.: SmartFall: an automatic fall detection system based on subsequence matching for the smartcane. In: Proceedings of the 4th International ICST Conference on Body Area Networks (2009)
Paiyarom, S., Tangamchit, P., Keinprasit, R., Kayasith, P.: Fall detection and activity monitoring system using dynamic time warping for elderly and disabled people. In: Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology - ICREATE 2009 (2009)
Acknowledgement
This study was supported by USA NSF SES-1314631, DUE-1303362, and STTR-1622788. Also, the authors thank Cathy Larson for the proofreading and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yu, S., Chen, H. (2017). Fall Detection with Orientation Calibration Using a Single Motion Sensor. 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_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-58877-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58876-6
Online ISBN: 978-3-319-58877-3
eBook Packages: Computer ScienceComputer Science (R0)