Abstract
We propose a multi-view onboard clustering of skeleton data for fall risk discovery. Clustering by an autonomous mobile robot opens the possibility for monitoring older adults from the most appropriate positions, respecting their privacies, and adapting to various changes. Since the data that the robot observes is a data stream and communication network can be unreliable, the clustering method in this case should be onboard. Motivated by the rapid increase of older adults in number and the severe outcomes of their falls, we adopt Kinect equipped robots and focus on gait skeleton analysis for fall risk discovery. Our key contributions are new between-skeleton distance measures for risk discovery and two series of experiments with our onboard clustering. The experiments revealed several key findings for the method and the application as well as interesting outcomes such as clusters which consist of unexpected risky postures.
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
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: Proc. VLDB 2003, pp. 81–92 (2003)
Aghajan, H., Augusto, J.C., Wu, C., McCullagh, P., Walkden, J.-A.: Distributed Vision-Based Accident Management for Assisted Living. In: Proc. Int. Conf. Smart Homes Health Telemat, pp. 196–205 (2007)
Ayers, D., Shah, M.: Monitoring Human Behavior from Video Taken in an Office Environment. Image and Vision Computing 19(12), 833–846 (2001)
Ball, A., Rye, D., Ramos, F., Velonaki, M.: Unsupervised Clustering of People from ‘Skeleton’ Data. In: Proc. HRI, pp. 225–226 (2012)
Clauser, C.E., McConville, J.T., Young, J.W.: Weight, Volume, and Center of Mass of Segments of the Human Body. Technical Report AMRL-TR-69-70, Wright Patterson Air Force Base, Ohio, USA (1969)
Comer, D.: The Ubiquitous B-Tree. ACM Computing Surveys 11(2), 121–137 (1979)
Coradeschi, S., et al.: GiraffPlus: Combining Social Interaction and Long Term Monitoring for Promoting Independent Living. In: Proc. HSI 2013, pp. 6–15 (2013)
Deguchi, Y., Suzuki, E.: Skeleton Clustering by Autonomous Mobile Robots for Subtle Fall Risk Discovery. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 500–505. Springer, Heidelberg (2014)
Deguchi, Y., Takayama, D., Takano, S., Scuturici, V.-M., Petit, J.-M., Suzuki, E.: Multiple-Robot Monitoring System Based on a Service-Oriented DBMS. In: Proc. Seventh ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2014) (2014)
DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C., Pregibon, D.: Squashing Flat Files Flatter. In: Proc. KDD 1999, pp. 6–15 (1999)
Fiore, L., Fehr, D., Bodor, R., Drenner, A., Somasundaram, G., Papanikolopoulos, N.: Multi-camera Human Activity Monitoring. J. Intelligent & Robotic Systems 52(1), 5–43 (2008)
Fischinger, D., et al.: HOBBIT - The Mutual Care Robot. In: Proc. ASROB (2013)
Gjoreski, H., Lustrek, M., Gams, M.: Context-Based Fall Detection Using Inertial and Location Sensors. In: Proc. AmI 2012, pp. 1–16 (2012)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering Data Streams: Theory and Practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)
Jain, A., Zhang, Z., Chang, E.Y.: Adaptive Non-Linear Clustering in Data Streams. In: Proc. CIKM 2006, pp. 122–131 (2006)
Kouno, A., Takayama, D., Suzuki, E.: Predicting the State of a Person by an Office-Use Autonomous Mobile Robot. In: Proc. 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2012), pp. 80–84 (2012)
Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining. Knowledge and Information Systems 29(2), 249–272 (2011)
Lühr, S., Lazarescu, M.: Incremental Clustering of Dynamic Data Streams using Connectivity Based Representative Points. Data Knowl. Eng. 68(1), 1–27 (2009)
Lutz, W., Sanderson, W., Scherbov, S.: The Coming Acceleration of Global Population Ageing. Nature 451(7179), 716–719 (2008)
Rashidi, P., Mihailidis, A.: A Survey on Ambient Assisted Living Tools for Older Adults. IEEE J. Biomedical and Health Informatics 17(3), 579–590 (2013)
Rubenstein, L.Z.: Falls in Older People: Epidemiology, Risk Factors and Strategies for Prevention. Age and Ageing 35(suppl. 2), ii37–ii41 (2006)
Seidl, T., Assent, I., Kranen, P., Krieger, R., Herrmann, J.: Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams. In: Proc. EDBT 2009, pp. 311–322 (2009)
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: Proc. CVPR 2011, pp. 1297–1304 (2011)
Stone, E.E., Skubic, M.: Evaluation of an Inexpensive Depth Camera for In-Home Gait Assessment. Journal of Ambient Intelligence and Smart Environments 3(4), 349–361 (2011)
Sugaya, S., Takayama, D., Kouno, A., Suzuki, E.: Intelligent Data Analysis by a Home-Use Human Monitoring Robot. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 381–391. Springer, Heidelberg (2012)
Suzuki, E., Deguchi, Y., Takayama, D., Takano, S., Scuturici, V.-M., Petit, J.-M.: Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots. In: Kawtrakul, A., Laurent, D., Spyratos, N., Tanaka, Y. (eds.) ISIP 2013. CCIS, vol. 421, pp. 57–70. Springer, Heidelberg (2014)
Suzuki, E., Matsumoto, E., Kouno, A.: Data Squashing for HSV Subimages by an Autonomous Mobile Robot. In: Ganascia, J.-G., Lenca, P., Petit, J.-M. (eds.) DS 2012. LNCS, vol. 7569, pp. 95–109. Springer, Heidelberg (2012)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: A New Data Clustering Algorithm and its Applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)
Acknowledgments
A part of this research was supported by a Bilateral Joint Research Project between Japan and France funded by JSPS and CNRS (CNRS/JSPS PRC 0672), and JSPS KAKENHI 24650070 and 25280085.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Takayama, D., Deguchi, Y., Takano, S., Scuturici, VM., Petit, JM., Suzuki, E. (2014). Multi-view Onboard Clustering of Skeleton Data for Fall Risk Discovery. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-14112-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14111-4
Online ISBN: 978-3-319-14112-1
eBook Packages: Computer ScienceComputer Science (R0)