Abstract
The project presented in this paper, has been developed by the cooperation between Competence Center for Applied Sensor Systems (ccass) at Darmstadt University of Applied Sciences, and Fraunhofer Institute for Production and Automation Engineering (Fraunhofer IPA) in Stuttgart. The target of this project is to innovate and develop an inertial-based tracking device for a low-cost patient monitoring system and tracking applications. This developed device is integrated with an inertial sensor system that enables it to measure the kinematic characteristics of a patient who wears this device. The initial-based device is able to detect emergency situation, such as falls, and simultaneously acquiring vital data for health-monitoring. When an emergency situation is detected, the integrated system sends a warning signal, the current position and vital-information of the patient wearing the device to their caretakers or nurses. This monitoring system is economically attractive for mass production, because it is compact, light-weight and low-cost. Moreover, this device can be used for a wide range of innovative applications in the industrial, medical and security sector.
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
Titterto, D.H., Weston, J.L.: Strapdown inertial navigation technology. The Institution of Electrical Engineers and the American Institute of Aeronautics and Astronautics (2004)
Bouten, C.V., Westerterp, K.R., Verduin, M., Janssen, J.D.: Assesment of energy expenditure for physical activity using a triaxisal acceleromenter. Journal of the American College of Sport Medicine (1994)
Duda, R.O., Hart, P.E., Strk, D.G.: Pattern Classification. Wiley (2001)
Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using bi-axial gyroscope sensor. Ph.D. dissertation. University of Limerick. Limerick. Ireland (2006)
Chobtrong, T., Haid, M., Guenes, E., Muenter, M., Kamil, M.: Bolt-identification using an IMU with Bayesian Decision Theory. In: Collaborative European Research Conference 2012 Darmstadt (2012)
Langley, P., Iba, W., Thomas, K.: An analysis of Bayesian classifiers. In: The Tenth National Conference of Artificial Intelligence (1992)
Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G.: Detection of daily physical activities using a triaxial accelerometer. Medical and Biological Engineering and Computing 41 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chobtrong, T., Haid, M., Kamil, M., Guenes, E. (2014). An Inertial-Based Person Tracking and Vital Data Acquiring for Low-Cost Patient Monitoring Systems. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-13102-3_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13101-6
Online ISBN: 978-3-319-13102-3
eBook Packages: Computer ScienceComputer Science (R0)