Abstract
We propose an activity recognition system for the elderly using a wearable sensor module embedding a tri-axial accelerometer, considering maximization of battery life. The sensor module embedding both a tri-axial acceleration sensor and an RF transmission module is worn at the right side of one’s waistband. It connects and transfers sensing data to subject’s PDA phone. Then, an algorithm on the PDA phone accumulates the data and classifies them as an activity. We utilize 3 tilts in addition to 3 acceleration values, compared to previous works. However, we reduce the sampling rate of the sensing data for saving battery life. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved 96% of accuracy in detecting an activity out of 9. It shows the proposed method can save the battery life without losing the recognition accuracy compared to related works.
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
Nyan, M.N., Francis, E., TAY, H., Manimaran, M., Seah, K.H.W.: Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer. Journal of Physics, Institute of Physics Publishing, 2006, Conference Series, vol. 34, pp.1059–1067 (2006)
Zhang, T., Wang, J., Xu, L., Liu, P.: Fall Detection by Wearable Sensor and One-Class SVM Algorithm. In: ICIC 2006. LNCIS, vol. 345, pp. 858–863 (2006)
Hwang, J.Y., Kang, J.M., Jang, Y.W., Kim, H.C.: ‘Development of Novel Algorithm and Real-time Monitoring Ambulatory System Using Bluetooth Module for Fall Detection in the Elderly. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM : a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, Sk., Jang, J., Park, S. (2008). An Efficient Method for Activity Recognition of the Elderly Using Tilt Signals of Tri-axial Acceleration Sensor. In: Helal, S., Mitra, S., Wong, J., Chang, C.K., Mokhtari, M. (eds) Smart Homes and Health Telematics. ICOST 2008. Lecture Notes in Computer Science, vol 5120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69916-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-69916-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69914-9
Online ISBN: 978-3-540-69916-3
eBook Packages: Computer ScienceComputer Science (R0)