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Development of a Self-Constructing Neuro-Fuzzy Inference System for Online Classification of Physical Movements

Jatoba, L.; Grossmann, U.; Ottenbacher, J.; Stork, W.; Mueller-Glaser, K. D.

This work is a part of the project "context- aware cardiac long-term monitoring (CALM)" of the Institute for information processing technology of the University of Karlsruhe. The aim of our research is the development of a system to help on the prevention of cardiovascular illnesses by continuous telemonitoring of patients. Therefore, the system should enable patient-friendly measurements of blood- pressure and electrocardiogram (ECG). Furthermore, it should allow an objective evaluation of the patient's physiological parameters taking into consideration context information. When evaluating cardiovascular parameters, the most important context information is physical activity. For obtaining the activity or movements of a patient, the authors propose a system based on acceleration sensors and adaptive neuro- fuzzy inference system (ANFIS) for pattern classification. Using this method, an online self-constructing activity recognition system has been developed and its results are shown.

Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Jahr 2007
Sprache Englisch
Identifikator ISBN: 1-4244-0942-X
KITopen-ID: 1000014316
Erschienen in 9th International Conference on e-Health Networking, Application and Services, 19-22 June 2007, Taipei, Taiwan
Verlag IEEE, Piscataway (NJ)
Seiten 332 - 335
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