Abstract
Falls are the leading cause of injury and death among older adults in the US. Computer vision systems offer a promising way of detecting falls. The present paper examines a fall detection and reporting system using the Microsoft Kinect sensor. Two algorithms for detecting falls are introduced. The first uses only a single frame to determine if a fall has occurred. The second uses time series data and can distinguish between falls and slowly lying down on the floor. In addition to detecting falls, the system offers several options for reporting. Reports can be sent as emails or text messages and can include pictures during and after the fall. A voice recognition system can be used to cancel false reports.
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
Alwan, M., Rajendran, P., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: 2nd Information and Communication Technologies, vol. 1, pp. 1003–1007 (2006)
Centers for Disease Control and Prevention. Falls Among Older Adults: An Overview, http://www.cdc.gov/HomeandRecreationalSafety/Falls/adultfalls.html
Khan, M.J., Habib, H.A.: Video Analytic for Fall Detection from Shape Features and Motion Gradients. In: Proceedings of the World Congress on Engineering and Computer Science, WCECS 2009, San Francisco, USA, October 20-22, vol. II (2009)
Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., Lundy, J.: Fall detection - principles and methods. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1663–1666 (2007)
Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 121–128. Springer, Heidelberg (2011)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 1297–1304. IEEE Computer Society, Washington, DC (2011)
Weisstein, E.W.: Point-Plane Distance. From MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/Point-PlaneDistance.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kawatsu, C., Li, J., Chung, C.J. (2013). Development of a Fall Detection System with Microsoft Kinect. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-37374-9_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37373-2
Online ISBN: 978-3-642-37374-9
eBook Packages: EngineeringEngineering (R0)