Abstract
This paper deals with Activities of Daily Living (ADL) Monitoring System. The proposed system takes into account deploying in real home. The important issue in deployment is the noninvasiveness. That is, the user should not feel inconvenience. Therefore, our system has been developed by making use of FSR sensors and an optional small body-activity sensor. In particular, FSR sensor is a typical noninvasive sensor since it has a shape of film. In order to make a light-weight monitoring system, we use as small number of sensors as possible. And we adopt rule-based ADL inferring algorithms to avoid inconvenience in collecting training data for supervised learning. For the purpose of improving the accuracy of occupation/usage detection, we make FSR sensors into FSR array sensors. We evaluate the proposed system in laboratory and real home environment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Allen, F., Ambikairajah, E., Lovell, N., Celler, B.: An adapted gaussian mixture model approach to accelerometry-based movement classification using time-domain features, pp. 3600–3603 (2006)
Elitecare, http://www.elitecare.com/
Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine 12(1), 20–26 (2008)
Intille, S.S., Bao, L., Tapia, E.M., Rondoni, J.: Acquiring in situ training data for context-aware ubiquitous computing applications. In: CHI 2004: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 1–8. ACM, New York (2004)
Kim, M., Bang, S.L., Song, S.k., Jang, J., Lim, J., Park, S.H., Park, S.J.: A novel system for inferring activities of daily living in smart home (2008)
Mathie, M.J., Coster, A.C., Lovell, N.H., Celler, B.G.: Daccelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement 25(2), 1–20 (2004)
NCI dictionary of cancer term, http://www.cancer.gov/dictionary/
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
United nations, long-range world population projections: based on the 1998 revision. the population division, department of economic and social affairs, united nations secretariat (2003)
Wilson, D., Atkeson, C.: Simultaneous tracking & activity recognition (star) using many anonymous, binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, M., Jang, J., Song, Sk., Jung, HY., Park, SH., Park, SJ. (2009). ADL Monitoring System Using FSR Arrays and Optional 3-Axis Accelerometer. In: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds) Ambient Assistive Health and Wellness Management in the Heart of the City. ICOST 2009. Lecture Notes in Computer Science, vol 5597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02868-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-02868-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02867-0
Online ISBN: 978-3-642-02868-7
eBook Packages: Computer ScienceComputer Science (R0)