Abstract
Life-logging has attracted rising attention as the most fundamental elements for developing every rich software today. This paper presents computational estimation and mapping of potential accidents and incidents of wheelchairs from life-logs with a single cheap and mini-sized three-axis accelerometer mounted on a wheelchair. Wheelchair driving data was obtained by real wheelchair users driving with their wheelchair on real roads, but has the sampling time delay and noises. As a first step of computational estimation, wheelchair driving behavior was classified into moving and static action, and the moving action was divided into tough and smooth status of the ground surface. We employed Support Vector Machine for classification, and made the precise supervised data from the video of wheelchair driving. As the result of classification, estimation of moving/static was achieved 98.2% accuracy rate and estimation of tough/smooth surface was achieved 82.6% accuracy rate. From the surface estimation result, wheelchair-driving difficulty was mapped and evaluated.
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.
Similar content being viewed by others
References
Yusuke, F., et al.: Sensing human movement of mobility and visually impaired people. In: ASSETS 2011 The Proceedings of the 13th International ACM SICACCESS Conference on Computers and Accessibility, pp. 279–280. ASSETS Press, NY (2011)
Coyle, E., et al.: Vibration-based terrain classification for electric powered wheelchairs. In: Telehealth/AT 2008 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies, pp. 139–144. ACTA Press, CA (2008)
Hashizume, T., et al.: Study on the wheelchair user’s body vibration and wheelchair driving torque when wheelchair is ascending/descending the boundary curb between pavement and roadway. In: SICE Annual Conference, pp. 1273–1276. IEEE Press, Tokyo (2008)
Mathie, M.J., et al.: Classification of basic daily movements using a triaxial accelerometer. Med. BioI. Eng. Comput. 42, 679–687 (2004)
Mathie, M.J., et al.: Detection of daily physical activities using a triaxial accelerometer. Med. BioI. Eng. Comput. 41, 296–301 (2003)
Noriaki, K., et al.: A study on ubiquitous system for collecting barrier-free information for wheelchair users. In: CASAME 2010 Proceedings of the 4th ACM International Workshop on Context-Awareness for Self-Managing Systems, Article No. 5. CASEMANS Press, NY (2010)
Zhenzheng, X., Jian, P., Eamonn, K.: A Brief Survey on Sequence Classification. ACM SIGKDD Explorations Newsletter 12(1), 40–48 (2010)
Leslie, C.S., Eskin, E., Noble, W.S.: The spectrum kernel: A string kernel for SVM protein classification. In: Pacific Symposium on Biocomputing, NY, pp. 566–575 (2002)
Ming, L., Sleep, R.: A robust approach to sequence classification. In: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2005, vol. 5, p. 201. IEEE Press, Hong Kong (2005)
Sonnenburg, S., Rätsch, G., Schäfer, C.: Learning Interpretable SVMs for Biological Sequence Classification. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 389–407. Springer, Heidelberg (2005)
Kampouraki, A., Manis, G., Nikou, C.: Heartbeat Time Series Classification With Support Vector Machines. IEEE Transactions on Information Technology in Biomedicine 13, 512–518 (2009)
Chen, W.-Y., et al.: Wheelchair-Related Accidents: Relationship With Wheelchair-Using Behavior in Active Community Wheelchair Users. Archives of Physical Medicine and Rehabilitation 92, 892–898 (2011)
Benjamin, S., et al.: A Survey of Outdoor Electric Powered Wheelchair Driving. In: RESNA Annual Conference, Las Vegas (2010)
Xiang, H., et al.: Wheelchair related injuries treated in US emergency departments. Inj. Prev. 12, 8–11 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iwasawa, Y., Yairi, I.E. (2012). Life-Logging of Wheelchair Driving on Web Maps for Visualizing Potential Accidents and Incidents. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-32695-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32694-3
Online ISBN: 978-3-642-32695-0
eBook Packages: Computer ScienceComputer Science (R0)