A Real-time Solider State and Performance Monitoring

and Predication via

Multi-modal Physiological and behavioral Signals

 

 

Developing on-line solider state and performance measurement techniques is a critical task for operational safety. The successful measurement can ensure the safety by identifying changes in human behavior before human performance deterioration happens. Physiological measures have been validated for operator state and performance predication.†† For this research, we propose a wireless transmission based real-time, portable, and low-cost GLUE-ON system and a multi-modality bio-signal analysis framework for solider state monitoring and performance predication. In the proposed GLUE-ON system, a set of low-cost physiological, behavioral, and environmental sensors (totally seven different-type sensors) are pasted onto two wearable devices (one is an arm-wearable belt and the other is a chest strap) to collect soliderís bio-signals and inspect the temperature change of the operational environment.

With the acquisition of the multi-modality bio-signal analysis, we then propose a multi-modality bio-signal analysis framework for solider state inspection and performance predication. The novel framework is developed to solve the issues on the three aspects: 1) achieve 1:1 mapping between bio-signal and operator state via distinctive feature extraction and selection; 2) avoid post-hoc analysis and extensive integration resources by using Dynamic Bayesian Network; 3) Intelligently and automatically select the most efficient sensors from the available sensors for cost-efficient solider state recognition and performance predication through active sensor selection.