Decision-aiding Algorithm for Interpreting Physiological Data in Hazardous Situations

A.B. Doser (USA)


Adaptive and clustering algorithms, Self-organizingmaps, clustering techniques, physiological monitoring.


A method is presented which combines self- organizing map techniques with a new clustering approach to interpret physiological data. The application is a decision making aid for team leaders of first responders (firefighters, police, etc.) who must decide which action to take regarding their team members in a stressful and dangerous situation. Actual physiological data was collected from volunteers who wore an armband collection device while undergoing regular activities. Results demonstrate that the scheme developed can be a useful tool to aid in the classification of individual body states. Additionally, results suggest that physiological data mapping is highly unique and that a network trained to one person does not readily transfer to another.

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